main_modul1-2-3_stromlastgang_ghd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modellierung von Stromlastgängen der Querschnittstechnologien im Sektor Gewerbe, Handel, Dienstleistungen (GHD) zur Fortschreibung und Potenzialanalyse der Nachfrageflexibilisierung\n",
    "\n",
    "Dieser Programmcode orientiert sich an drei Modulen nach Böckmann u.a. (2021) sowie Seim u.a. (2021):\n",
    "    1. Entwicklung branchen- und technologiespezifischer Lastprofile des deutschen GHD-Sektors im Jahr 2018\n",
    "    2. Fortschreibung der Lastprofile bis 2035 anhand von zwei literaturbasierten Energieverbrauchsszenarien\n",
    "    3. Quantifizierung zeitlich hochaufgelöster Nachfrageflexibilisierungspotenziale mithilfe eines Ansatzes nach Kleinhans, (2014)\n",
    "\n",
    "Das Programm verfügt über zwei Hauptmethoden (Lastgangsynthese und Szenario-Nachfrageflexi). Die erste Hauptmethode entspricht je nach gewählten Verhaltensparametern einem Modellschritt des ersten Moduls. Die zweite Hauptmethode führt Module zwei und drei aus. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "firstRun = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "firstRun ist eine Hilfsvariable, die sicherstellt, dass beim \"Run File (Run All)\" nicht die Main-Methoden\n",
    "ausgeführt werden. Zur Benutzung bitte wie folgt vorgehen:\n",
    "    0. Pfad zur Input- und Output-Umgebung aktualisieren (siehe unten \"Settings\").\n",
    "    1. Run File - Damit werden alle Excel- und SQL-Daten importiert sowie die Objekte initialisiert.\n",
    "    2. Settings auf gewünschte Funktion einstellen und Zelle ausführen.\n",
    "    3. Jeweilige Main Methode einzeln mithilfe \"Run Cell\" (Strg+Enter) ausführen.\n",
    "    \n",
    "Bitte zwischen den Veränderungen der Settings alle lokal gespeicherten Variablen löschen.\n",
    "Idealerweise Vorgehen in der Reihenfolge der Arbeit:\n",
    "    1. Lastgangsynthese | Settings: [ownAssumptions: False, doOptimize: False (Bottom-up-Lastprofile)]\n",
    "    2. Lastgangsynthese | Settings: [ownAssumptions: True, doOptimize: False (Lastprofile mit strukturellen Annahmen)]\n",
    "    3. Lastgangsynthese | Settings: [ownAssumptions: True, doOptimize: True (Lastprofile mit strukturellen Annahmen nach einer Hyperparameteroptimierung)]\n",
    "    4. Szenario - Nachfrageflexi\n",
    "\n",
    "Hinweis: Alle Einheiten der Berechnung entsprechen den Einheiten der verwendeten Anwendungsbilanzen.\n",
    "Innerhalb dieser Arbeit liegen alle AWBs in PJ/a vor, weshalb die Einheit der Lastprofile zunächst ebenfalls PJ entspricht.\n",
    "\n",
    "### Modellierungskürzel:\n",
    "\n",
    "\n",
    "Die ersten beiden Modellierungsschritte des ersten Moduls der Arbeit sind mit Annahmen verbunden. Im ersten Schritt, der Entwicklung von Bottom-up-Lastprofilen, werden literaturbasierte Annahmen als Teil der Technologiedaten getroffen. Im zweiten Schritt werden die Lastprofile um strukturelle Annahmen ergänzt, die aus einem Vergleich mit den Branchenlastprofilen des Forschungsprojekts \"DemandRegio\" abgeleitet werden. Die genauen Annahmen sind in Abschnitt 4.1 der Arbeit beschrieben. Zur besseren Nachvollziehbarkeit der Modellierung ist jede Annahme mit einem Kürzel innerhalb dieses Programms versehen.\n",
    "\n",
    "Die nachfolgenden Tabellen, die auch als Excel-Tabelle dem elektronischen Anhang beiliegen, fassen diese Kürzel zusammen. "
   ]
  },
  {
   "attachments": {
    "LiteraturbasierteAnnahmen.PNG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![LiteraturbasierteAnnahmen.PNG](attachment:LiteraturbasierteAnnahmen.PNG)"
   ]
  },
  {
   "attachments": {
    "StrukturelleAnnahmenBLP.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![StrukturelleAnnahmenBLP.PNG](attachment:StrukturelleAnnahmenBLP.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "from numpy import genfromtxt\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import get_ipython\n",
    "import seaborn as sns\n",
    "import xlsxwriter\n",
    "from datetime import datetime\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.metrics import r2_score\n",
    "import os\n",
    "import math\n",
    "import sqlalchemy\n",
    "get_ipython().run_line_magic('matplotlib', 'qt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model:\n",
    "\n",
    "Klasse, mit der Jahresinstanzen der Gleichzeitigkeitsfaktoren erzeugt werden.<br>\n",
    "    a: 96 Werte an a-Tagen<br>\n",
    "    b: 96 Werte an b-Tagen<br>\n",
    "    c: 96 Werte an c-Tagen<br>\n",
    "    d: 365*96 Werte in Jahresreihenfolge<br>\n",
    "    total: numpy.stack(datetime, schedule, jahreswerte, ...)<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(self, a, b, c, jahr, total):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.c = c\n",
    "        self.jahr = jahr\n",
    "        self.total = total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings:\n",
    "<br>\n",
    "Hier können Verhaltenoptionen des Modells eingestellt werden. \n",
    "\n",
    "##### Bitte unbedingt vor dem erstmaligen Ausführen den Pfad der Input- und Output-Dateien anpassen. Der Ordner \"Excel_Input\" des elektronischen Anhangs muss sich in dieser Umgebung befinden. Außerdem bitte vor jedem kompletten Ausführen des Modells (Run File) die zwischengespeicherten Variablen löschen. \n",
    "\n",
    "Empfehlenswert ist die Ausführung einzelner Zellen, da die Importe mitunter rechenintensiv sind und im Fall von \"SQL Weather Import\" einen Internetanschluss benötigen.<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "absDemand = True   # True: JahresverbrÃ¤uche werden absolut ermittelt. False: Nur Gleichzeitigkeitsfaktoren werden in Profilen berechnet\n",
    "doOutput = True   # Ob Excel-Output erzeugt werden soll, sollte bei True stehen\n",
    "ownAssumptions = True   # / True Bottom-up - Ob eigene Annahmen mit einbezogen werden sollen oder nicht\n",
    "doOptimize = True   # /True Bottom-up - Ob eine Optimisierung der Hyperparameter factSia, factDIn, factGS durchgeführt wird\n",
    "defaultFactSia = 0.5    # Starteinstellung (LambdaSIA)\n",
    "defaultFactDin = 0.5    # Starteinstellung (LambdaDIN)\n",
    "defaultFactGS = {85: 0.8, 6471: 0.5, 86: 0.2, 47: 0, 55: 0.8}    # Starteinstellung (LambdaGS) - Vgl. Tabelle 4.3 - Bei 47 wird GS ohnehin nicht beachtet wegen AL_47.1 und AL_47.2 \n",
    "inputEnv = 'C:/Users/xy/data_in' # Bitte anpassen\n",
    "outpathEnv = 'C:/Users/xy/Output'  # Bitte anpassen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescaling-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_to(inputlist, n):\n",
    "    \"\"\"\n",
    "    Rescaling Funktion, die eine Liste mit linearer Interpolation auf gewünschte\n",
    "    Länge streckt. In diesem Programm verwendet, um Listen mit stündlichen Werten mittels\n",
    "    linearer Interpolation auf viertelstündliche Werte zu strecken.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    inputlist : list\n",
    "        Liste, die reskaliert werden soll.\n",
    "    n : int\n",
    "        Ganzzahl n, die die Länge der reskalierten Liste bestimmt.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    out : list\n",
    "          Die reskalierte Liste.\n",
    "\n",
    "    \"\"\"\n",
    "    long = [0] * n\n",
    "    m = len(inputlist)\n",
    "    for i, x in enumerate(inputlist):\n",
    "        long[i*(n)//(m)] = x\n",
    "    count = 0\n",
    "    for i in range(n):\n",
    "        if i < n-4:\n",
    "            if count == 0:\n",
    "                m = long[i+4] - long[i]\n",
    "                yachsenabschnitt = long[i]\n",
    "            long[i] = m*count + yachsenabschnitt\n",
    "            count = count + 0.25\n",
    "            if count == 1:\n",
    "                count = 0\n",
    "        elif i >= n-4:\n",
    "            long[i] = long[n-4]\n",
    "    out = long\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solarradiation-Influence-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def influence_transform_sol(inputlist, i_max, i_min):\n",
    "    \"\"\"\n",
    "    Influence Funktion - für Solarradiation: Stellt den Einfluss der Solarradiation als\n",
    "    Funktion dar, mit der die Beleuchtung multipliziert werden kann. Wenig Solarradiation bildet ab\n",
    "    auf 1 und viel Solarradiation auf 0. Begrenzt über maximalem Schwellenwert\n",
    "    auf 0 und unter minimalem Schwellenwert auf 1. Dazwischen erfolgt eine lineare Skalierung.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    inputlist : list\n",
    "        Liste mit Werten der Solarradiation.\n",
    "    i_max : TYPE\n",
    "        Oberer Schwellenwert, über dem die Transformation 0 abbildet.\n",
    "    i_min : TYPE\n",
    "        Unterer Schwellenwert, unter dem die Transformation 1 abbildet.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    solarlist : list\n",
    "        Die transformierte Funktion des Einflusses der Solarradiation auf die Beleuchtung.\n",
    "\n",
    "    \"\"\"\n",
    "    solarlist = inputlist.copy()\n",
    "    for i in range(len(solarlist)):\n",
    "        if solarlist[i] > i_max:\n",
    "            solarlist[i] = 0\n",
    "        elif solarlist[i] < i_min:\n",
    "            solarlist[i] = 1\n",
    "        else:\n",
    "            solarlist[i] = (i_max - solarlist[i])/(i_max - i_min)\n",
    "    return solarlist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperatur-Influence-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def influence_transform_ac(inputlist):\n",
    "    \"\"\"\n",
    "    Influence Funktion - für Temperatur (Klimakälte): Begrenzt über maximalem Schwellenwert (24) auf 1\n",
    "    und unter minimalem Schwellenwert (21) auf 0.\n",
    "    Dazwischen lineare Skalierung zwischen 0,6 und 1. Quelle: Ladwig, (2017), S. 51, 197\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    inputlist : list\n",
    "        Liste der Außenlufttemperatur.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    aclist : list\n",
    "        Liste der Gleichzeitigkeitsfaktoren der Klimakälte.\n",
    "\n",
    "    \"\"\"\n",
    "    aclist = inputlist.copy()\n",
    "    for i in range(len(aclist)):\n",
    "        if aclist[i] > 24:\n",
    "            aclist[i] = 1\n",
    "        elif aclist[i] < 21:\n",
    "            aclist[i] = 0\n",
    "        elif 21 <= aclist[i] <= 24:\n",
    "            aclist[i] = (aclist[i]*(0.4/(24-21))-2.2)\n",
    "    return aclist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wärme-Influence-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def influence_transform_heating(inputlist, wz):\n",
    "    \"\"\"\n",
    "    Influence-Funktion - für Temperatur (Raumwärme): Begrenzt über oberem Schwellenwert\n",
    "    (entnommen aus DIN 18599-10) auf 0 und darunter lineare Skalierung bis 0° Celsius.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    inputlist : list\n",
    "        Liste der Außenlufttemperaturwerte, die transformiert werden soll.\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftsszweigs nach Klassifikation der Wirtschaftszweige (WZ 2008).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    heatlist : list\n",
    "        Liste der Gleichzeitigkeitsfaktoren der Raumwärme.\n",
    "\n",
    "    \"\"\"\n",
    "    heatlist = inputlist.copy()\n",
    "    if wz in {6471, 55, 47, 85, 56, 91, 90}:    # Entnommen aus DIN18599-10\n",
    "        threshold = 21\n",
    "    if wz == 86:\n",
    "        threshold = 22\n",
    "    if wz == 93:\n",
    "        threshold = 19\n",
    "        \n",
    "    for i in range(len(heatlist)):  # ASH_2\n",
    "        if 0 <= heatlist[i] and heatlist[i] < threshold:\n",
    "            heatlist[i] = (heatlist[i]-threshold)/(0-threshold)\n",
    "        if heatlist[i] < 0:\n",
    "            heatlist[i] = 1\n",
    "        if heatlist[i] >= threshold:\n",
    "            heatlist[i] = 0\n",
    "            \n",
    "    return heatlist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annahme-Einfügen-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert_assumption(inputlist, time_start, time_end, value):\n",
    "    \"\"\"\n",
    "    Insert Funktion - um annahmenbasierte Gleichzeitigkeitsfaktoren in einen vorhandenen Lastgang\n",
    "    einzufügen und den Übergang zu den Originalwerten zu glätten.\n",
    "    Parameters\n",
    "    ----------\n",
    "    inputlist : list\n",
    "        Liste der ursprünglichen Gleichzeitigkeitsfaktoren.\n",
    "    time_start : float\n",
    "        Zeitpunkt des Beginns der einzufügenden Annahme in Stunden.\n",
    "    time_end : float\n",
    "        Zeitpunkt des Endes der einzufügenden Annahme in Stunden.\n",
    "    value : float\n",
    "        Einzufügender Dynamisierungsfaktor während des vorgegebenen Zeitraums.\n",
    "    Returns\n",
    "    -------\n",
    "    x : list\n",
    "        Liste der neuen Gleichzeitigkeitsfaktoren.\n",
    "    \"\"\"\n",
    "    ts = max((4*time_start)-1, 0)\n",
    "    te = (4*time_end)-1\n",
    "    \n",
    "    x = numpy.array(inputlist, dtype='float64').copy()\n",
    "    y = 0\n",
    "    count = 0.25\n",
    "    og_s = x[ts]\n",
    "    m = float(value - og_s)\n",
    "    for i in range(4):\n",
    "        x[ts+y] = og_s + m*count\n",
    "        x[te+y] = value + (-1*m)*count\n",
    "        count = count+0.25\n",
    "        y = y+1\n",
    "        \n",
    "    x[ts+4:te] = value\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zeitraum-Durchschnitts-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_avg_resJahres(resJahresArg, tag1, tag2, sched):\n",
    "    \"\"\"\n",
    "    get_avg_Resulting_Jahres-Funktion - berechnet die durchschnittlichen\n",
    "    Lasten innerhalb eines Zeitraums.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    resJahresArg : list\n",
    "        Liste der Last des gesamten Jahres.\n",
    "    tag1 : string\n",
    "        Datum in Datetime Format, Start des zu mittelnden Zeitraums.\n",
    "    tag2 : string\n",
    "        Datum in Datetime Format, Ende des zu mittelnden Zeitraums.\n",
    "    sched : string\n",
    "        Typtag als String ('a', 'b', 'c'), über den gemittelt wird.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    out : list\n",
    "        Liste der Länge 96, also einem durchschnittlichen Tag.\n",
    "\n",
    "    \"\"\"\n",
    "    out = numpy.zeros(96)\n",
    "    daycount = 0\n",
    "    tsTag1 = pd.Timestamp(tag1)\n",
    "    tsTag2 = pd.Timestamp(tag2)\n",
    "    for i in range(365):\n",
    "        if i in range(tsTag1.dayofyear, tsTag2.dayofyear) and sched in resJahresArg[i*96, 1]:\n",
    "            out = out + resJahresArg[i*96:(i+1)*96, 2]\n",
    "            daycount = daycount+1\n",
    "    out = out/daycount\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create-DataFrame-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_DataFrame(modeltotal):\n",
    "    \"\"\"\n",
    "    Erstellt aus einem Modell ein komplettes Pandas DataFrame mit Flags wie\n",
    "    Saison, Typtag und den Querschnittstechnologien. Wird in der Funktion model_energy_demand verwendet. \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    modeltotal : Model\n",
    "        Eine Instanz der Klasse 'Model'.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    datasetTotal : DataFrame\n",
    "        Das komplette DataFrame.\n",
    "\n",
    "    \"\"\"\n",
    "    day = pd.date_range(\"00:00\", \"23:45\", freq=\"15min\").strftime('%H:%M')\n",
    "    winterA = ((get_avg_resJahres(modeltotal, '2018-01-01', '2018-02-28', 'a') +\n",
    "                get_avg_resJahres(modeltotal, '2018-12-01', '2018-12-31', 'a'))/2)\n",
    "    winterB = ((get_avg_resJahres(modeltotal, '2018-01-01', '2018-02-28', 'b') +\n",
    "                get_avg_resJahres(modeltotal, '2018-12-01', '2018-12-31', 'b'))/2)\n",
    "    winterC = ((get_avg_resJahres(modeltotal, '2018-01-01', '2018-02-28', 'c') +\n",
    "                get_avg_resJahres(modeltotal, '2018-12-01', '2018-12-31', 'c'))/2)\n",
    "    fhA = ((get_avg_resJahres(modeltotal, '2018-03-01', '2018-05-31', 'a') +\n",
    "            get_avg_resJahres(modeltotal, '2018-09-01', '2018-11-30', 'a'))/2)\n",
    "    fhB = ((get_avg_resJahres(modeltotal, '2018-03-01', '2018-05-31', 'b') +\n",
    "            get_avg_resJahres(modeltotal, '2018-09-01', '2018-11-30', 'b'))/2)\n",
    "    fhC = ((get_avg_resJahres(modeltotal, '2018-03-01', '2018-05-31', 'c') +\n",
    "            get_avg_resJahres(modeltotal, '2018-09-01', '2018-11-30', 'c'))/2)\n",
    "    sommerA = get_avg_resJahres(modeltotal, '2018-06-01', '2018-08-30', 'a')\n",
    "    sommerB = get_avg_resJahres(modeltotal, '2018-06-01', '2018-08-30', 'b')\n",
    "    sommerC = get_avg_resJahres(modeltotal, '2018-06-01', '2018-08-30', 'c')\n",
    "    dWA = pd.DataFrame({'Zeit': day, 'Tagtyp': 'a', 'Saison': 'Winter', 'Werte': winterA})\n",
    "    dWB = pd.DataFrame({'Zeit': day, 'Tagtyp': 'b', 'Saison': 'Winter', 'Werte': winterB})\n",
    "    dWC = pd.DataFrame({'Zeit': day, 'Tagtyp': 'c', 'Saison': 'Winter', 'Werte': winterC})\n",
    "    dFHA = pd.DataFrame({'Zeit': day, 'Tagtyp': 'a', 'Saison': 'FrÃ¼hling & Herbst', 'Werte': fhA})\n",
    "    dFHB = pd.DataFrame({'Zeit': day, 'Tagtyp': 'b', 'Saison': 'FrÃ¼hling & Herbst', 'Werte': fhB})\n",
    "    dFHC = pd.DataFrame({'Zeit': day, 'Tagtyp': 'c', 'Saison': 'FrÃ¼hling & Herbst', 'Werte': fhC})\n",
    "    dSA = pd.DataFrame({'Zeit': day, 'Tagtyp': 'a', 'Saison': 'Sommer', 'Werte': sommerA})\n",
    "    dSB = pd.DataFrame({'Zeit': day, 'Tagtyp': 'b', 'Saison': 'Sommer', 'Werte': sommerB})\n",
    "    dSC = pd.DataFrame({'Zeit': day, 'Tagtyp': 'c', 'Saison': 'Sommer', 'Werte': sommerC})\n",
    "    datasetTotal = pd.concat([dWA, dWB, dWC, dFHA, dFHB, dFHC, dSA, dSB, dSC])\n",
    "    return datasetTotal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APE-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ape(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Absolute Percentage Error Funktion - gibt einen Numpy-array der individuellen\n",
    "    absoluten prozentualen Abweichung jeder Zeitscheibe zurück. Der Durchschnittswert aller APE\n",
    "    ist dann der MAPE.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true : list\n",
    "        Liste der Trainingsdaten (im Rahmen dieses Modells die Branchenlastgänge\n",
    "                                  des Demand Regio Projektes).\n",
    "    y_pred : list\n",
    "        Liste der Testdaten (im Rahmen dieses Modells die bottom-up synthetisierten\n",
    "                             Lastgänge einer Branche).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    numpy.array\n",
    "        Array der individuellen prozentualen Abweichungen.\n",
    "\n",
    "    \"\"\"\n",
    "    y_true, y_pred = numpy.array(y_true), numpy.array(y_pred)\n",
    "    \n",
    "    return numpy.abs((y_true - y_pred) / y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAPE-Funktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Mean Absolute Percentage Error Funktion - gibt ein Skalar der gemittelten\n",
    "    absoluten prozentualen Abweichungen zurück.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true : list\n",
    "        Liste der Trainingsdaten (im Rahmen dieses Modells die Branchenlastgänge\n",
    "                                  des Demand Regio Projektes).\n",
    "    y_pred : list\n",
    "        Liste der Testdaten (im Rahmen dieses Modells die bottom-up synthetisierten\n",
    "                             Lastgänge einer Branche).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        Skalar der gemittelten absoluten prozentualen Abweichung.\n",
    "\n",
    "    \"\"\"\n",
    "    y_true, y_pred = numpy.array(y_true), numpy.array(y_pred)\n",
    "    \n",
    "    return numpy.mean(numpy.abs((y_true - y_pred) / y_true))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DSM-Leistungsfunktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dsm_p(qst, timeMaxCapDays):\n",
    "    \"\"\"\n",
    "    Funktion, die die Leistung der maximalen Lade- oder Entladerate - Pmax und Pmin -\n",
    "    berechnet. Dazu wird der Lastgang einer Querschnittstechnologie als Argument übergeben\n",
    "    und der Zeitraum definiert, aus dem die Maximalkapazität der Technologie entnommen wird.\n",
    "    Hierzu eignet sich der Zeitraum eines Tages, um saisonspezifischen, also temperatur- oder\n",
    "    solarradiationsabhängigen Peaks gerecht zu werden.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    qst : list\n",
    "        Liste des Lastgangs einer Querschnittstechnologie.\n",
    "    timeMaxCapDays : int\n",
    "        Ganzzahl des Zeitraums, innerhalb dessen die maximale Kapazität ausgewählt wird.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pmax : numpy.array\n",
    "        Liste der zu jedem Zeitpunkt maximal möglichen Lasterhöhung [PJ].\n",
    "    pmin : numpy.array\n",
    "        Liste der zu jedem Zeitpunkt maximal möglichen Lastverringerung [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "    t = 24*4\n",
    "    pmax = numpy.zeros(len(qst))\n",
    "    pmin = numpy.zeros(len(qst))\n",
    "    for i in range(len(qst)):\n",
    "        y = math.floor(i/t)     # Findet die Zahl des aktuellen Tages (0-364)\n",
    "        \"\"\"\n",
    "        Als maximale Kapazität wird die Maximallast des jeweiligen Tages verwendet\n",
    "        (in anderen Veröffentlichungen wird stattdessen die maximal installierte Leistung als Maximalkapazität modelliert)\n",
    "        \"\"\"\n",
    "        maxCap = qst[y*t:(y + timeMaxCapDays)*t].max()   \n",
    "        pmax[i] = (maxCap - qst[i])\n",
    "        pmin[i] = ((-1) * qst[i])\n",
    "    return pmax, pmin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DSM-Energiefunktion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dsm_e(qst, timeframeHours):\n",
    "    \"\"\"\n",
    "    Funktion, die den Energieinhalt des Energiespeicheräquivalents\n",
    "    der DSM-Maßnahme beschreibt. Diese ist das Integral der Laderaten Pmax und Pmin über einen festgelegten\n",
    "    Zeitraum der maximalen Verschiebedauer.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    qst : list\n",
    "        Liste des Lastgangs einer Querschnittstechnologie.\n",
    "    timeframeHours : int\n",
    "        Ganzzahl der Stunden der maximalen Verschiebedauer.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    emax : numpy.array\n",
    "        Maximaler Energieinhalt des Energiespeicheräquivalents der DSM-Maßnahme [PJ/4].\n",
    "    emin : numpy.array\n",
    "        Minimaler Energieinhalt des Energiespeicheräquivalents der DSM-Maßnahme [PJ/4].\n",
    "\n",
    "    \"\"\"\n",
    "    t = timeframeHours*4\n",
    "    emax = numpy.zeros(len(qst))\n",
    "    emin = numpy.zeros(len(qst))\n",
    "    for i in range(len(qst)):\n",
    "        if (i+t) > (len(qst)-1):\n",
    "            emax[i] = (qst[((len(qst)-1)-t):(len(qst)-1)].sum() / 4)\n",
    "        if (i+t) <= (len(qst)-1):\n",
    "            emax[i] = (qst[i:(i+t)].sum() / 4)      # geteilt durch 4, um auf Stundenwerte zu kommen\n",
    "        if i < t:\n",
    "            emin[i] = (-1) * (qst[(t-t):t].sum() / 4)\n",
    "        if i >= t:\n",
    "            emin[i] = (-1) * (qst[(i-t):i].sum() / 4)\n",
    "    return emax, emin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel-Importe:\n",
    "\n",
    "Import der Excel Dateien für\n",
    "- Anwesenheitsprofil der ISO Norm 18523-1 (Occupancy Iso)\n",
    "- Beleuchtungsprofil der ISO Norm 18523-1 (Lighting Iso)\n",
    "- Anwesenheitsprofil der SIA Norm Occupancy SIA\n",
    "- Anwesenheitsprofile der DIN Norm DIN 18599-10:2018-09\n",
    "- Klimakälteprofil nach Ladwig, (2018)\n",
    "- Kalenderdaten\n",
    "- Anwendungsbilanz des Jahres 2018\n",
    "- Branchenlastprofile des Forschungsprojekts \"DemandRegio\"\n",
    "- Mechanische Energie in Krankenhäusern nach Hagemeier, (2017)\n",
    "- Anwendungsbilanz Referenzszenario 2035\n",
    "- Anwendungsbilanz Basisszenario 2035\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfLight = pd.read_excel((inputEnv+'/occupancy-iso2016energy.xlsx'),\n",
    "                        sheet_name='Lighting')\n",
    "dfOccIso = pd.read_excel((inputEnv+'/occupancy-iso2016energy.xlsx'),\n",
    "                         sheet_name='Occupancy')\n",
    "dfShares = pd.read_excel((inputEnv+'/occupancy-iso2016energy.xlsx'),\n",
    "                         sheet_name='BuildingShares')\n",
    "dfOccSia = pd.read_excel((inputEnv+'/occupancy-SIA2015.xlsx'),\n",
    "                         sheet_name='Occupancy')\n",
    "dfCal = pd.read_excel((inputEnv+'/schedule_2018_15min.xlsx'),\n",
    "                      sheet_name='main')\n",
    "dfOccDin = pd.read_excel((inputEnv+'/DIN18599-10.xlsx'),\n",
    "                         sheet_name='Python_Export')\n",
    "dfHvac = pd.read_excel((inputEnv+'/hvac-ladwig2018demand.xlsx'),\n",
    "                       sheet_name='Python_Export')\n",
    "dfAnwend = pd.read_excel((inputEnv+'/anwendungsbilanzen_fraunisi2015envbghd.xlsx'),\n",
    "                         sheet_name='Python_Export')\n",
    "dfBrLast = pd.read_excel((inputEnv+'/demandRegio_Branchenlastmodelle.xlsx'),\n",
    "                         sheet_name='Tabelle1')\n",
    "dfMechEn86 = pd.read_excel((inputEnv+'/mechEn-86-hagemeier2018messdaten.xlsx'),\n",
    "                           sheet_name='Python_Export')\n",
    "dfRefSzen = pd.read_excel((inputEnv+'/Szenarien/referenzszenario_fraunisi2017langfristM3.xlsx'),\n",
    "                          sheet_name='Python_Export')\n",
    "dfBasSzen = pd.read_excel((inputEnv+'/Szenarien/basisszenario_fraunisi2017langfristM3.xlsx'),\n",
    "                          sheet_name='Python_Export')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Typtag-Kodierung nach ISO:\n",
    "a = Wochentag\n",
    "b = Samstag\n",
    "c = Sonntag, Feiertag\n",
    "\"\"\"\n",
    "# Create an empty list\n",
    "lightingIsoHourly = []\n",
    "occupancyIsoHourly = []\n",
    "occupancySiaHourly = []\n",
    "buildingSharesIso = []\n",
    "buildingSharesSia = []\n",
    "buildingSharesDin = []\n",
    "calender = []\n",
    "occupancyDinHourly = []\n",
    "hvacHourly = []\n",
    "ambientcool = []\n",
    "ventilation = []\n",
    "mechEn86Hourly = []\n",
    "mechEn86A = []\n",
    "mechEn86BC = []\n",
    "nachtspeicherheiz = []\n",
    "waermepumpe = []\n",
    "\n",
    "\n",
    "for i in range((dfLight.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    lightingIsoHourly.append(list(dfLight.iloc[i, :]))\n",
    "\n",
    "for i in range((dfOccIso.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    occupancyIsoHourly.append(list(dfOccIso.iloc[i, :]))\n",
    "\n",
    "for i in range((dfOccSia.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    occupancySiaHourly.append(list(dfOccSia.iloc[i, :]))\n",
    "\n",
    "# Iterate over each row in Calender\n",
    "for i in range((dfCal.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    calender.append(list(dfCal.iloc[i, :]))\n",
    "\n",
    "for i in range((dfShares.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    buildingSharesIso.append(list(dfShares.iloc[i, :]))\n",
    "\n",
    "for i in range((dfOccDin.shape[0])):\n",
    "    # Using iloc to access the values of\n",
    "    # the current row denoted by \"i\"\n",
    "    occupancyDinHourly.append(list(dfOccDin.iloc[i, :]))\n",
    "\n",
    "for i in range((dfHvac.shape[0])):\n",
    "    hvacHourly.append(list(dfHvac.iloc[i, :]))\n",
    "\n",
    "for i in range((dfMechEn86.shape[0])):\n",
    "    mechEn86Hourly.append(list(dfMechEn86.iloc[i, :]))\n",
    "\n",
    "# Hourly in Quarterly umwandeln\n",
    "occupancyIsoQuarterly = occupancyIsoHourly.copy()\n",
    "for zeile in range(len(occupancyIsoQuarterly)):\n",
    "    occupancyIsoQuarterly[zeile] = occupancyIsoQuarterly[zeile][:5] + rescale_to(occupancyIsoQuarterly[zeile][5:],96)\n",
    "\n",
    "lightingIsoQuarterly = lightingIsoHourly.copy()\n",
    "for zeile in range(len(lightingIsoQuarterly)):\n",
    "    lightingIsoQuarterly[zeile] = lightingIsoQuarterly[zeile][:5] + rescale_to(lightingIsoQuarterly[zeile][5:],96)\n",
    "\n",
    "occupancySiaQuarterly = occupancySiaHourly.copy()\n",
    "for zeile in range(len(occupancySiaQuarterly)):\n",
    "    occupancySiaQuarterly[zeile] = occupancySiaQuarterly[zeile][:3] + rescale_to(occupancySiaQuarterly[zeile][3:27],96)\n",
    "    buildingSharesSia.append(occupancySiaHourly[zeile][1::26])\n",
    "\n",
    "occupancyDinQuarterly = occupancyDinHourly.copy()\n",
    "for zeile in range(len(occupancyDinQuarterly)):\n",
    "    occupancyDinQuarterly[zeile] = occupancyDinQuarterly[zeile][:3]+rescale_to(occupancyDinQuarterly[zeile][3:27],96)\n",
    "    buildingSharesDin.append(occupancyDinHourly[zeile][1::26])\n",
    "\n",
    "hvacQuarterly = hvacHourly.copy()\n",
    "for zeile in range(len(hvacQuarterly)):\n",
    "    if hvacQuarterly[zeile][0] == \"ambient_cooling\":\n",
    "        coolingLT = hvacQuarterly[zeile][25] #lower threshold\n",
    "        coolingHT = hvacQuarterly[zeile][26] #higher threshold\n",
    "        ambientcool = rescale_to(hvacQuarterly[zeile][1:25], 96)\n",
    "    if hvacQuarterly[zeile][0] == \"ventilation\":\n",
    "        ventilation = rescale_to(hvacQuarterly[zeile][1:25], 96)\n",
    "    if hvacQuarterly[zeile][0] == \"nachtspeicherheizung\":\n",
    "        nachtspeicherheiz = rescale_to(hvacQuarterly[zeile][1:25], 96)\n",
    "    if hvacQuarterly[zeile][0] == \"waermepumpe\":\n",
    "        waermepumpe = rescale_to(hvacQuarterly[zeile][1:25], 96)\n",
    "\n",
    "for zeile in range(len(mechEn86Hourly)):\n",
    "    if mechEn86Hourly[zeile][0] == \"MechEnA\":\n",
    "        mechEn86A = rescale_to(mechEn86Hourly[zeile][1:25], 96)\n",
    "    if mechEn86Hourly[zeile][0] == \"MechEnBC\":\n",
    "        mechEn86BC = rescale_to(mechEn86Hourly[zeile][1:25], 96)\n",
    "\n",
    "#Building Shares in Dictionary überführen\n",
    "buildingSharesIsoDict = dict(numpy.array(buildingSharesIso)[:,1:3])\n",
    "buildingSharesSiaDict = dict(numpy.array(buildingSharesSia))\n",
    "buildingSharesDinDict = dict(numpy.array(buildingSharesDin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL-Weather-Import:\n",
    "\n",
    "Import regionalisierter Wetterdaten aus dem Forschungsprojekt \"DemandRegio\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlalchemy.create_engine('postgresql://demandregio_read:read_demandregio@81.169.223.134/demandregio')  # Lesezugriff!\n",
    "\n",
    "\n",
    "sql_tp_all = \"\"\" select * FROM demandregio.demandregio_temporal as a\n",
    "                  join demandregio.t_nuts3_lk as b on a.id_region=b.id_ags\n",
    "                  WHERE id_temporal = 11  and year_base = 2018\n",
    "                  \"\"\"\n",
    "\n",
    "sql_sr_all = \"\"\" select * FROM demandregio.demandregio_temporal as a\n",
    "                  join demandregio.t_nuts3_lk as b on a.id_region=b.id_ags\n",
    "                  WHERE id_temporal = 17  and year_base = 2018\n",
    "\n",
    "                  \"\"\"\n",
    "\n",
    "\n",
    "tp_sql = pd.read_sql(sql_tp_all, con)  # tp stands for temperature\n",
    "sr_sql = pd.read_sql(sql_sr_all, con)  # sr stands for solar radiation\n",
    "\n",
    "tp_sql[\"ags_lk\"] = tp_sql[\"ags_lk\"].astype(float)\n",
    "sr_sql[\"ags_lk\"] = sr_sql[\"ags_lk\"].astype(float)\n",
    "\n",
    "bzeLK = pd.read_excel((inputEnv+'/BZEjeLK.xlsx'), sheet_name='BZEjeLK.xlsx')\n",
    "bzeLK.loc[88] = bzeLK.sum()\n",
    "\n",
    "for zeile in range(0, bzeLK.shape[0]):\n",
    "    summe = bzeLK.iloc[zeile, 1]\n",
    "    for spalte in bzeLK:\n",
    "        if spalte != \"WZ\" and spalte != \"SUM\":\n",
    "            bzeLK[spalte][zeile] = numpy.divide(bzeLK[spalte][zeile], (summe))  # Anteile der Landkreise an GesamtBZE der WZ\n",
    "\n",
    "bzeLK = bzeLK.loc[88]\n",
    "\n",
    "temperatureHourly = numpy.zeros(365*24)\n",
    "globalSolar = numpy.zeros(365*24*4)\n",
    "\n",
    "for lk in sr_sql['ags_lk']:\n",
    "    if lk in bzeLK.index:\n",
    "        tp_lk = numpy.asarray(numpy.array(tp_sql.loc[(tp_sql['ags_lk'] == lk) &\n",
    "                               (tp_sql['internal_id'].astype('str') == '[1]') &\n",
    "                               (tp_sql['year_base'] == 2018)]['values'])[0])\n",
    "\n",
    "        tp_lk = tp_lk * bzeLK[int(lk)]\n",
    "        temperatureHourly = temperatureHourly+ tp_lk\n",
    "\n",
    "\n",
    "        sr_lk = numpy.asarray(numpy.array(sr_sql.loc[(sr_sql['ags_lk'] == lk) &\n",
    "                                       (tp_sql['year_base'] == 2018)]['values'])[0])\n",
    "\n",
    "        sr_lk = sr_lk * bzeLK[int(lk)]\n",
    "        globalSolar = globalSolar + sr_lk\n",
    "\n",
    "\n",
    "temperature = temperatureHourly.copy()\n",
    "temperature = numpy.repeat(temperatureHourly, 4)\n",
    "for j in range((365*24)-1):\n",
    "    y = temperature[j*4+4]\n",
    "    x = temperature[j*4]\n",
    "    k = (y - x)/4\n",
    "    temperature[(j*4)+1] = x + (k*1)\n",
    "    temperature[(j*4)+2] = x + (k*2)\n",
    "    temperature[(j*4)+3] = x + (k*3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calender:\n",
    "\n",
    "Hinzufügen der Datums- und Zeittagsinformationen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "npcal = numpy.array(calender)\n",
    "\n",
    "dateTime = npcal[:, 5]\n",
    "\n",
    "counter = 0\n",
    "for i in range(len(dateTime)):\n",
    "    dateTime[i] = dateTime[i] + pd.Timedelta(minutes=15*counter)\n",
    "    counter = counter+1\n",
    "    if counter == 96:\n",
    "        counter = 0\n",
    "\n",
    "schedule = npcal[:, 7]\n",
    "\n",
    "tm = []\n",
    "for i in range(35040):\n",
    "    tm.append(npcal[i, 5].strftime(format=\"%H:%M\"))\n",
    "time = numpy.array(tm)\n",
    "\n",
    "dte = []\n",
    "for i in range(35040):\n",
    "    dte.append(npcal[i, 5].strftime(format=\"%Y-%m-%d\"))\n",
    "date = numpy.array(dte)\n",
    "\n",
    "wd = []\n",
    "for i in range(35040):\n",
    "    wd.append(datetime.strptime(date[i], \"%Y-%m-%d\").isoweekday())\n",
    "weekday = numpy.array(wd)\n",
    "\n",
    "seas = []\n",
    "for i in range(35040):\n",
    "    if i <= (58*96) or i> (333*96): #Zahl der Tage ausgerechnet und mit Viertelstunden multipliziert\n",
    "        seas.append(\"Winter\")\n",
    "    elif (58*96)<i<(151*96) or (242*96)<i<(334*96):\n",
    "        seas.append(\"Frühling u Herbst\")\n",
    "    elif (150*96)<i<(243*96):\n",
    "        seas.append(\"Sommer\")\n",
    "season = numpy.array(seas)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anwesenheits-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_occupancy_profile(wz,factSia, factDin):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Anwesenheiten.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    factSia : float\n",
    "        Anteil zwischen SIA und ISO Normen, begrenzt auf Intervall [0,1]. (LambdaSIA)\n",
    "    factDin : float\n",
    "        Anteil der Minderung von Anwesenheiten außerhalb der DIN Norm, begrenzt auf Intervall [0,1].(LambdaDIN)\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    occModel : Model\n",
    "        Instanz der \"Model\"-Klasse. Enthält Anwesenheitsprofile nach Typtagkodierung\n",
    "        sowie für das gesamte Jahr 2018.\n",
    "\n",
    "    \"\"\"\n",
    "    npOccSia = numpy.array(occupancySiaQuarterly).copy()\n",
    "    npOccIso = numpy.array(occupancyIsoQuarterly).copy()\n",
    "    npOccIsofloat = numpy.array(npOccIso[:, 5:].copy(), dtype='float64')\n",
    "    npOccDin = numpy.array(occupancyDinQuarterly).copy()\n",
    "    npOccDinfloat = numpy.array(npOccDin[:, 3:].copy(), dtype='float64')\n",
    "    npOccSiafloat = numpy.array(npOccSia[:, 3:].copy(), dtype='float64')\n",
    "    occDin = []\n",
    "    \"\"\"\n",
    "    Wirtschaftszweig auswählen und über mehrere mitteln für DIN\n",
    "    \"\"\"\n",
    "    for i in range(len(npOccDin)):\n",
    "        if float(npOccDin[i, 2]) == wz:\n",
    "            occDin.append(list(float(buildingSharesDinDict[npOccDin[i, 1]])*npOccDinfloat[i, :]))\n",
    "    npOccDin = numpy.array(occDin)\n",
    "    npOccDin = numpy.array(numpy.sum(npOccDin, axis=0))\n",
    "    \"\"\"\n",
    "    Occupancy Iso\n",
    "    \"\"\"\n",
    "    occIsoA = []\n",
    "    occIsoB = []\n",
    "    occIsoC = []\n",
    "    for i in range(len(npOccIso)):\n",
    "        if float(npOccIso[i, 4]) == wz and npOccIso[i, 2] == 'a':\n",
    "            occIsoA.append(list(float(buildingSharesIsoDict[npOccIso[i, 1]])*npOccIsofloat[i, :]))\n",
    "        if float(npOccIso[i, 4]) == wz and npOccIso[i, 2] == 'b':\n",
    "            occIsoB.append(list(float(buildingSharesIsoDict[npOccIso[i, 1]])*npOccIsofloat[i, :]))\n",
    "        if float(npOccIso[i, 4]) == wz and npOccIso[i, 2] == 'c':\n",
    "            occIsoC.append(list(float(buildingSharesIsoDict[npOccIso[i, 1]])*npOccIsofloat[i, :]))\n",
    "    np_occA = numpy.array(occIsoA).astype(numpy.float)\n",
    "    np_occB = numpy.array(occIsoB).astype(numpy.float)\n",
    "    np_occC = numpy.array(occIsoC).astype(numpy.float)\n",
    "    if len(occIsoA) > 1:\n",
    "        occIsoA = numpy.array(numpy.sum(np_occA, axis=0))\n",
    "        occIsoB = numpy.array(numpy.sum(np_occB, axis=0))\n",
    "        occIsoC = numpy.array(numpy.sum(np_occC, axis=0))\n",
    "    elif len(occIsoA) == 1:\n",
    "        occIsoA = np_occA\n",
    "        occIsoB = np_occB\n",
    "        occIsoC = np_occC\n",
    "    occIsoJahrList = []\n",
    "    for i in range(365):\n",
    "        if schedule[i*96] == 'a':\n",
    "            occIsoJahrList.extend(float(i) for i in occIsoA)\n",
    "        if schedule[i*96] == 'b':\n",
    "            occIsoJahrList.extend(float(i) for i in occIsoB)\n",
    "        if schedule[i*96] == 'c':\n",
    "            occIsoJahrList.extend(float(i) for i in occIsoC)\n",
    "    occIsoJahr = numpy.array(occIsoJahrList).astype(numpy.float)\n",
    "    \"\"\"\n",
    "    Occupancy aus Iso mit reinnehmen und mit SIA vermengen\n",
    "    \"\"\"\n",
    "    occSiaList = []\n",
    "    for i in range(len(npOccSia)):\n",
    "        if float(npOccSia[i, 2]) == wz:\n",
    "            occSiaList.append(list(float(buildingSharesSiaDict[npOccSia[i,1]])*npOccSiafloat[i, :]))\n",
    "    np_occSia = numpy.array(occSiaList)\n",
    "    occSiaA = numpy.array(numpy.sum(np_occSia, axis= 0))\n",
    "    \"\"\"\n",
    "    Elemente von DIN Anwesenheiten ändern 1->0 und 0->1\n",
    "    \"\"\"\n",
    "    npOccDin = (1-npOccDin)\n",
    "    occSiaIsoA = ((factSia*occSiaA) + ((1-factSia)*occIsoA))  # Mischung aus SIA und ISO\n",
    "    occSiaIsoA = occSiaIsoA - ((factDin*npOccDin)*occSiaIsoA)\n",
    "    occIsoB = occIsoB - ((factDin*npOccDin)*occIsoB)\n",
    "    occIsoC = occIsoC - ((factDin*npOccDin)*occIsoC)\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Annahmen einbauen\n",
    "    \"\"\"\n",
    "    if wz == 47:\n",
    "        if ownAssumptions:\n",
    "            occIsoB = occSiaIsoA    # AO_47.2\n",
    "\n",
    "    if wz == 6471:\n",
    "        occIsoB = insert_assumption(occIsoB, 8, 19, 0.3).copy()  # AO_6471.1\n",
    "        occIsoB = insert_assumption(occIsoB, 19, 23, 0).copy()\n",
    "\n",
    "    if wz == 47:\n",
    "        for i in range(len(occIsoC)):\n",
    "            if occIsoC[i] > 0:\n",
    "                if ownAssumptions:\n",
    "                    occIsoC[i] = 0   # AO_47.1\n",
    "\n",
    "    if wz == 85:\n",
    "        if ownAssumptions:\n",
    "            occIsoB = occIsoC   # AO_85.1\n",
    "            occSiaIsoA[4*12:4*15] = numpy.clip(occSiaIsoA[4*12:4*15], 0, 0.9) # AO_85.2\n",
    "            \n",
    "    if wz == 55:\n",
    "        if ownAssumptions:\n",
    "            occIsoB = occSiaIsoA   # AO_55.1\n",
    "            occIsoC = occSiaIsoA\n",
    "\n",
    "    occSiaIsoJahrList = []\n",
    "    for i in range(365):\n",
    "        if schedule[i*96] == 'a':\n",
    "            occSiaIsoJahrList.extend(float(i) for i in occSiaIsoA)\n",
    "        if schedule[i*96] == 'b':\n",
    "            occSiaIsoJahrList.extend(float(i) for i in occIsoB)\n",
    "        if schedule[i*96] == 'c':\n",
    "            occSiaIsoJahrList.extend(float(i) for i in occIsoC)\n",
    "    occJahr = numpy.array(occSiaIsoJahrList).astype(numpy.float)\n",
    "\n",
    "    occTotal = numpy.stack((date, time, weekday, season, schedule, occJahr), axis=-1)\n",
    "\n",
    "    occModel = Model(occSiaIsoA, occIsoB, occIsoC, occJahr, occTotal)\n",
    "\n",
    "    return occModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beleuchtungs-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "globSol = influence_transform_sol(globalSolar, 40, 20)\n",
    "def model_lighting_demand(wz, occModel, factGS, anwBil):\n",
    "    \n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Beleuchtung aus.\n",
    "    - Wetter (Global Solar)\n",
    "    - Jahresgesamtverbrauch Strom Beleuchtung GHD aus Anwendungsbilanz/Szenario\n",
    "    - Wirtschaftszweig\n",
    "    \n",
    "    Achtung, anwBil muss DataFrame sein, eingelesen per excel\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "     wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    factGS : float\n",
    "        Anteil des Einflusses der Solarstrahlung auf die Beleuchtung (LambdaGS), begrenzt auf dem Intervall [0,1].\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lightModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur Beleuchtung [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    #Achtung! Standardisieren - und dann alle faktoren mit täglichem Durchschnittsverbrauch multiziplieren\n",
    "    anwBel = float(anwBil.loc[anwBil['WZ'] == wz, 'Beleuchtung'])\n",
    "    jahresBel = numpy.array([anwBel/(365*96)] * (365*96))\n",
    "    npLight = numpy.array(lightingIsoQuarterly).copy()\n",
    "                                                                                                                #-> devJahr\n",
    "    lightA = numpy.array(occModel.a).copy()\n",
    "    lightB = numpy.array(occModel.b).copy()\n",
    "    lightC = numpy.array(occModel.c).copy()\n",
    "    \"\"\"\n",
    "    Annahmen einfügen\n",
    "    \"\"\"\n",
    "    if wz == 6471:\n",
    "        if ownAssumptions:\n",
    "            lightA[4*10:4*14] = numpy.clip(lightA[4*10:4*14], 0.9, 1)  # AL_6471.2\n",
    "        for i in range(69, 79): #17.25*4,19.75*4 #AL_6471.3\n",
    "            lightA[i] = lightA[i-1]-0.039   # stetige Abnahme bis 19:30 auf 0.2\n",
    "        for i in range(79, 93):\n",
    "            lightA[i] = lightA[i-1]-0.017   # stetige Abnahme bis 23h (Verkehrsflächen)\n",
    "        for i in range(5*4, 29):\n",
    "            lightA[i] = lightA[i-1]+0.029  # morgendlicher Anstieg in einigen Betrieben\n",
    "    if wz == 55:                            # AL_55.1\n",
    "        lightA = insert_assumption(lightA, 1, 6, 0.2).copy()\n",
    "        lightB = insert_assumption(lightB, 1, 6, 0.2).copy()\n",
    "        lightC = insert_assumption(lightC, 1, 6, 0.2).copy()\n",
    "    if wz == 85:\n",
    "        if ownAssumptions:\n",
    "            lightA[4*10:4*14] = numpy.clip(lightA[4*10:4*14], 0.9,1) # AL_85.1\n",
    "            lightA[57] = 0.8\n",
    "    if wz == 47:\n",
    "        smoothing = {1: 0.25, 2: 0.4, 3: 0.6, 4: 0.75}\n",
    "        for lst in [lightA, lightB, lightC]:\n",
    "            count = 0\n",
    "            for i in range(len(lst)):\n",
    "                if lst[i] > 0.1:    # to enable effects of AL_47.1\n",
    "                    count = count + 1\n",
    "                    if count <= 4:   # AL_47.1\n",
    "                        lst[i] = smoothing[count]\n",
    "                    else:\n",
    "                        lst[i] = 1      # AL_47.2\n",
    "                        \n",
    "    if wz == 86:                            # AL_86.1\n",
    "        lightA = insert_assumption(lightA, 0, 6, 0.2).copy()\n",
    "        lightB = insert_assumption(lightA, 0, 6, 0.2).copy()\n",
    "        lightC = insert_assumption(lightA, 0, 6, 0.2).copy()\n",
    "        lightA = insert_assumption(lightA, 22, 0, 0.2).copy()\n",
    "        lightB = insert_assumption(lightA, 22, 0, 0.2).copy()\n",
    "        lightC = insert_assumption(lightA, 22, 0, 0.2).copy()\n",
    "        lightA[23*4:] = 0.2\n",
    "        lightA[:4] = 0.2  \n",
    "        lightB[:4] = 0.2  \n",
    "        lightC[:4] = 0.2  \n",
    "        lightB[23*4:] = 0.2\n",
    "        lightC[23*4:] = 0.2\n",
    "        lightA[0:4] = 0.2\n",
    "        lightB[0:4] = 0.2\n",
    "        lightC[0:4] = 0.2\n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    Jahresprofil erstellen\n",
    "    \"\"\"\n",
    "    lightJahrList = []\n",
    "    for i in range(365):\n",
    "        if schedule[i*96] == 'a':\n",
    "            lightJahrList.extend(float(i) for i in lightA)\n",
    "        if schedule[i*96] == 'b':\n",
    "            lightJahrList.extend(float(i) for i in lightB)\n",
    "        if schedule[i*96] == 'c':\n",
    "            lightJahrList.extend(float(i) for i in lightC)\n",
    "    lightJahr = numpy.array(lightJahrList).astype(numpy.float)\n",
    "\n",
    "\n",
    "    productLight = (lightJahr)+(lightJahr*globSol*factGS)\n",
    "    dynFacBel = productLight/numpy.amax(productLight)\n",
    "\n",
    "    if wz == 47: #AL_47.3, AL_55.2,\n",
    "        for i in range(len(dynFacBel)):\n",
    "            if dynFacBel[i] < 0.05:\n",
    "                dynFacBel[i] = 0.05 \n",
    "\n",
    "    if wz == 85  or wz == 55: #AL_85.2\n",
    "        for i in range(len(dynFacBel)):\n",
    "            if dynFacBel[i] < 0.15:\n",
    "                dynFacBel[i] = 0.15\n",
    "\n",
    "    if wz == 6471: #AL_6471.4\n",
    "        for i in range(len(dynFacBel)):\n",
    "            if dynFacBel[i] < 0.2:\n",
    "                dynFacBel[i] = 0.2\n",
    "\n",
    "    if absDemand is True:\n",
    "        resJahresBel = (sum(jahresBel) / sum(dynFacBel)) * dynFacBel\n",
    "    else:\n",
    "        resJahresBel = dynFacBel.copy()\n",
    "\n",
    "    # komplette Matrix: 0=Datum u Zeit, 1=schedule, 2=resJahresBel\n",
    "    resBelTotal = numpy.stack((date, time, weekday, season, schedule, resJahresBel), axis=-1)\n",
    "    lightModel = Model(lightA, lightB, lightC, resJahresBel, resBelTotal)\n",
    "\n",
    "    return lightModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Klimakälte-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = numpy.zeros(96)\n",
    "acTemp = influence_transform_ac(temperature)\n",
    "npAC = numpy.array(ambientcool).copy()\n",
    "def model_ambient_cooling_demand(wz, occModel, anwBil):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Klimakälte.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    acModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur Klimakälte [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    anwKlimaK = float(anwBil.loc[anwBil['WZ'] == wz, 'Klimakälte'])\n",
    "    jahresKlimaK = numpy.array([anwKlimaK/(365*96)] * (365*96))\n",
    "\n",
    "    acJahrList = []\n",
    "    for i in range(365):\n",
    "        acJahrList.extend(float(i) for i in npAC)\n",
    "    acJahr = numpy.array(acJahrList)\n",
    "\n",
    "    productAC = (acJahr * acTemp) * occModel.jahr\n",
    "    dynFacAC = productAC/numpy.amax(productAC)\n",
    "    if absDemand == True:\n",
    "        resAC = dynFacAC*(sum(jahresKlimaK)/sum(dynFacAC))\n",
    "    else:\n",
    "        resAC = dynFacAC.copy()\n",
    "\n",
    "    resACTotal = numpy.stack((date, time, weekday, season, schedule, resAC), axis=-1)\n",
    "    acModel = Model(dummy, dummy, dummy, resAC, resACTotal)\n",
    "\n",
    "\n",
    "    return acModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prozesskälte-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = numpy.zeros(96)\n",
    "def model_process_cooling_demand(wz, occModel, anwBil):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Prozesskälte.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pcoolModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur Prozesskälte [PJ].\n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    anwProzK = float(anwBil.loc[anwBil['WZ'] == wz, 'Prozesskälte'])\n",
    "    jahresProzK = numpy.array([anwProzK/(365*96)] * (365*96))\n",
    "\n",
    "    pcoolA = numpy.array(occModel.a).astype(float).copy()\n",
    "    pcoolB = numpy.array(occModel.b).astype(float).copy()\n",
    "    pcoolC = numpy.array(occModel.c).astype(float).copy()\n",
    "\n",
    "    for lst in [pcoolA, pcoolB, pcoolC]:\n",
    "        for i in range(len(lst)):\n",
    "            if lst[i] > 0:\n",
    "                lst[i] = 1\n",
    "            if lst[i] == 0:\n",
    "                if wz == 47:\n",
    "                    lst[i] = 0.7    #APC_47.1\n",
    "                else:\n",
    "                    lst[i] = 0.8    #APC_1\n",
    "\n",
    "    pcoolB = pcoolB*0.95    #APC_2\n",
    "    pcoolC = pcoolC*0.9     #APC_3\n",
    "\n",
    "    for lst in [pcoolA, pcoolB, pcoolC]:\n",
    "        for i in range(len(lst)):   #APCSH_1\n",
    "            if i < (len(lst)-4):\n",
    "                if (lst[i+1] > lst[i] and lst[i] == lst[i-1]):\n",
    "                    lst[i+1] = lst[i]+ (0.25*(lst[i+4] - lst[i]))\n",
    "                    lst[i+2] = lst[i]+ (0.5*(lst[i+4] - lst[i]))\n",
    "                    lst[i+3] = lst[i]+ (0.75*(lst[i+4] - lst[i]))\n",
    "                if (lst[i+1] < lst[i] and lst[i] == lst[i-1]):\n",
    "                    lst[i+1] = lst[i] - (0.5*(lst[i]-lst[i+3]))\n",
    "                    lst[i+2] = lst[i] - (0.75*(lst[i]-lst[i+3]))\n",
    "                    lst[i] = lst[i] - (0.25*(lst[i]-lst[i+3]))\n",
    "\n",
    "    \"\"\"\n",
    "    Date        No. of Day\n",
    "    31.01.2018\t30\n",
    "    28.02.2018\t58\n",
    "    31.05.2018\t150\n",
    "    31.08.2018\t242\n",
    "    30.11.2018\t333\n",
    "    31.12.2018  365\n",
    "    \"\"\"\n",
    "\n",
    "    pcoolJahrList = []\n",
    "    for i in range(365):\n",
    "        if schedule[i*96] == 'a':\n",
    "            temp = pcoolA\n",
    "        if schedule[i*96] == 'b':\n",
    "            temp = pcoolB\n",
    "        if schedule[i*96] == 'c':\n",
    "            temp = pcoolC\n",
    "        if i <= 58 or i> 333: #Winter\n",
    "            temp = temp * 0.8   #APC_4\n",
    "        if 58<i<151 or 242<i<334: #Übergang\n",
    "            temp = temp * 0.9\n",
    "        pcoolJahrList.extend(temp)\n",
    "    pcoolJahr = numpy.array(pcoolJahrList)\n",
    "\n",
    "    dynFacPcool = pcoolJahr/numpy.amax(pcoolJahr)\n",
    "    if absDemand == True:\n",
    "        resAC = dynFacPcool*(sum(jahresProzK)/sum(dynFacPcool))\n",
    "    else:\n",
    "        resAC = dynFacPcool.copy()\n",
    "\n",
    "    pcoolTotal = numpy.stack((date, time, weekday, season, schedule, resAC), axis=-1)\n",
    "    pcoolModel = Model(pcoolA, pcoolB, pcoolC, resAC, pcoolTotal)\n",
    "    return pcoolModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raumwärme-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "npNSH = numpy.array(nachtspeicherheiz)\n",
    "npWP = numpy.array(waermepumpe)\n",
    "\n",
    "def model_space_heating_demand(wz, occModel, anwBil, wpshare):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Raumwärme und Warmwasser.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "    wpshare : float\n",
    "        Anteil der Wärmepumpen, begrenzt auf dem Intervall [0,1].\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    heatModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zu Raumwärme und Warmwasser [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    npHeat = ((wpshare * npWP)+( (1-wpshare) * npNSH))\n",
    "    anwHeat = float(anwBil.loc[anwBil['WZ'] == wz, 'Raumwärme und Warmwasser'])\n",
    "    jahresHeat = numpy.array([anwHeat/(365*96)] * (365*96))\n",
    "\n",
    "    waterA = occModel.a.copy()\n",
    "    waterB = occModel.b.copy()\n",
    "    waterC = occModel.c.copy()\n",
    "\n",
    "    heatJahrList = []\n",
    "    for i in range(365):\n",
    "        heatJahrList.extend(float(i) for i in npHeat)\n",
    "    heatJahr = numpy.array(heatJahrList)\n",
    "\n",
    "    for lst in [waterA, waterB, waterC]:\n",
    "        for i in range(len(lst)):\n",
    "            if lst[i] > 0:\n",
    "                lst[i] = 1\n",
    "\n",
    "    waterJahrList = []\n",
    "    for i in range(365):\n",
    "        if schedule[i*96] == 'a':\n",
    "            waterJahrList.extend(float(i) for i in waterA)\n",
    "        if schedule[i*96] == 'b':\n",
    "            waterJahrList.extend(float(i) for i in waterB)\n",
    "        if schedule[i*96] == 'c':\n",
    "            waterJahrList.extend(float(i) for i in waterC)\n",
    "    waterJahr = numpy.array(waterJahrList).astype(numpy.float)\n",
    "\n",
    "    for i in range(len(waterJahr)):  #APCSH_1\n",
    "        if i < (len(waterJahr)-4):\n",
    "            if (waterJahr[i] == 0 and waterJahr[i+1]>0):\n",
    "                waterJahr[i+1] = 0.25*waterJahr[i+4]\n",
    "                waterJahr[i+2] = 0.5*waterJahr[i+4]\n",
    "                waterJahr[i+3] = 0.75*waterJahr[i+4]\n",
    "            if (waterJahr[i] > 0 and waterJahr[i+1] == 0):\n",
    "                waterJahr[i] = 0.25*waterJahr[i-3]\n",
    "                waterJahr[i-1] = 0.5*waterJahr[i-3]\n",
    "                waterJahr[i-2] = 0.75*waterJahr[i-3]\n",
    "\n",
    "    tempinf = influence_transform_heating(temperature, wz)  # ASH_2\n",
    "    productHeat = (0.3*(heatJahr * tempinf)) + (0.7*waterJahr)# ASH_1\n",
    "\n",
    "    dynFacHeat = productHeat/numpy.amax(productHeat)\n",
    "    if absDemand == True:\n",
    "        resHeat = dynFacHeat*(sum(jahresHeat)/sum(dynFacHeat))\n",
    "    else:\n",
    "        resHeat = dynFacHeat.copy()\n",
    "\n",
    "    heatTotal = numpy.stack((date, time, weekday, season, schedule, resHeat), axis=-1)\n",
    "    heatModel = Model(dummy, dummy, dummy, resHeat, heatTotal)\n",
    "    return heatModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mechanische-Energie-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_mechenergy_demand(wz, occModel, anwBil):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der mechanischen Energie.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    mechModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur mechanischen Energie [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    handelA = numpy.array(occModel.a).copy()\n",
    "    handelB = numpy.array(occModel.b).copy()\n",
    "    handelC = numpy.array(occModel.c).copy()\n",
    "\n",
    "    for lst in [handelA, handelB, handelC]:\n",
    "        for i in range(len(lst)):\n",
    "            if lst[i] > 0:    # to enable effects of AM_47.1\n",
    "                lst[i] = 0.76\n",
    "            else:\n",
    "                lst[i] = 0\n",
    "\n",
    "    vent = numpy.array(ventilation).copy()\n",
    "    anwMech = float(anwBil.loc[anwBil['WZ'] == wz, 'Mechanische Energie'])\n",
    "    jahresMech = numpy.array([anwMech/(365*96)] * (365*96))\n",
    "\n",
    "    mEA = numpy.array(mechEn86A).copy()\n",
    "    mEB = numpy.array(mechEn86BC).copy()\n",
    "    mEC = numpy.array(mechEn86BC).copy()\n",
    "\n",
    "    vent = 0.24*vent    # AM_1\n",
    "    mEA = 0.76*mEA\n",
    "    mEB = 0.76*mEB\n",
    "    mEC = 0.76*mEC\n",
    "    if wz == 86:\n",
    "        mechEnA = (mEA + vent)\n",
    "        mechEnB = (mEB + vent)\n",
    "        mechEnC = (mEC + vent)\n",
    "        mechEnL = []\n",
    "        for i in range(365):\n",
    "            if schedule[i*96] == 'a':\n",
    "                mechEnL.extend(float(i) for i in mechEnA)\n",
    "            if schedule[i*96] == 'b':\n",
    "                mechEnL.extend(float(i) for i in mechEnB)\n",
    "            if schedule[i*96] == 'c':\n",
    "                mechEnL.extend(float(i) for i in mechEnC)\n",
    "        mechEnList = numpy.array(mechEnL).astype(numpy.float)\n",
    "\n",
    "    if wz == 47:\n",
    "        if ownAssumptions:\n",
    "            mechEnHA = (handelA + vent) \n",
    "            mechEnHB = (handelB + vent)\n",
    "            mechEnHC = (handelC + vent)\n",
    "            mechEnHL = []\n",
    "            for i in range(365):\n",
    "                if schedule[i*96] == 'a':\n",
    "                    mechEnHL.extend(float(i) for i in mechEnHA)\n",
    "                if schedule[i*96] == 'b':\n",
    "                    mechEnHL.extend(float(i) for i in mechEnHB)\n",
    "                if schedule[i*96] == 'c':\n",
    "                    mechEnHL.extend(float(i) for i in mechEnHC)\n",
    "            mechEnList = numpy.array(mechEnHL).astype(numpy.float)\n",
    "        if not ownAssumptions:\n",
    "            mechEn = numpy.ones(96)*0.76\n",
    "            mechEn = mechEn + vent\n",
    "            mechEnList = []\n",
    "            for i in range(365):\n",
    "                mechEnList.extend(float(i) for i in mechEn)\n",
    "\n",
    "\n",
    "    if wz != 86 and wz !=55 and wz !=47:\n",
    "        mechEn = numpy.ones(96)*0.76\n",
    "        mechEn = mechEn + vent\n",
    "        mechEnList = []\n",
    "        for i in range(365):\n",
    "            mechEnList.extend(float(i) for i in mechEn)\n",
    "            \n",
    "    if wz == 55: \n",
    "        mechEn = numpy.ones(96)*0.5*0.76 # AM_55.1\n",
    "        mechEn[40:68] = 0.76\n",
    "        z=0\n",
    "        tempcount = 0.125\n",
    "        incl = 0.38\n",
    "        for i in range(8):\n",
    "            mechEn[33+z] = 0.38 + incl*tempcount\n",
    "            mechEn[68+z] = 0.76 + (-1*incl)*tempcount\n",
    "            tempcount = tempcount+0.125\n",
    "            z = z+1\n",
    "        mechEn = mechEn + vent\n",
    "        mechEnList = []\n",
    "        for i in range(365):\n",
    "            mechEnList.extend(float(i) for i in mechEn)\n",
    "\n",
    "    productMech = numpy.array(mechEnList)\n",
    "    dynFacMech = productMech/numpy.amax(productMech)\n",
    "    if absDemand == True:\n",
    "        resMechEn = productMech*(sum(jahresMech)/sum(dynFacMech))\n",
    "    else:\n",
    "        resMechEn = productMech.copy()\n",
    "\n",
    "    mechTotal = numpy.stack((date, time, weekday, season, schedule, resMechEn), axis=-1)\n",
    "    mechModel = Model(dummy, dummy, dummy, resMechEn, mechTotal)\n",
    "    return mechModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IKT-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_ikt_demand(wz, occModel, anwBil):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der IKT.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    iktModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur IKT [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    if wz != 47 and wz != 6471 and wz != 85:\n",
    "        iktJahr = numpy.ones(35040)\n",
    "\n",
    "    if wz == 47 or wz == 6471 or wz == 85:\n",
    "        iktA = occModel.a.copy()\n",
    "        iktB = occModel.b.copy()\n",
    "        iktC = occModel.c.copy()\n",
    "        for lst in [iktA, iktB, iktC]:\n",
    "            for i in range(len(lst)):\n",
    "                if lst[i] > 0:\n",
    "                    lst[i] = 1\n",
    "                if lst[i] == 0:\n",
    "                    if wz == 47 and ownAssumptions:\n",
    "                        lst[i] = 0.2 #AIKT_47.1\n",
    "                    if wz == 6471 or wz == 85:\n",
    "                        lst[i] = 0.6 #AIKT_6471, AIKT_85\n",
    "\n",
    "        for lst in [iktA, iktB, iktC]:\n",
    "            for i in range(len(lst)):   #APCSH_1\n",
    "                if i < (len(lst)-4):\n",
    "                    if (lst[i+1] > lst[i] and lst[i] == lst[i-1]):\n",
    "                        lst[i-1] = lst[i]+ (0.5*(lst[i+4] - lst[i]))\n",
    "                        lst[i-2] = lst[i]+ (0.25*(lst[i+4] - lst[i]))\n",
    "                        lst[i] = lst[i]+ (0.75*(lst[i+4] - lst[i]))\n",
    "                    if (lst[i+1] < lst[i] and lst[i] == lst[i-1]):\n",
    "                        lst[i-1] = lst[i] - (0.5*(lst[i]-lst[i+1]))\n",
    "                        lst[i-2] = lst[i] - (0.25*(lst[i]-lst[i+1]))\n",
    "                        lst[i] = lst[i] - (0.75*(lst[i]-lst[i+1]))\n",
    "\n",
    "        iktJahrList = []\n",
    "        for i in range(365):\n",
    "            if schedule[i*96] == 'a':\n",
    "                iktJahrList.extend(float(i) for i in iktA)\n",
    "            if schedule[i*96] == 'b':\n",
    "                iktJahrList.extend(float(i) for i in iktB)\n",
    "            if schedule[i*96] == 'c':\n",
    "                iktJahrList.extend(float(i) for i in iktC)\n",
    "        iktJahr = numpy.array(iktJahrList).astype(numpy.float)\n",
    "\n",
    "\n",
    "\n",
    "    if wz == 47 and ownAssumptions == False:\n",
    "        iktJahr = numpy.ones(35040)\n",
    "\n",
    "    anwIKT = float(anwBil.loc[anwBil['WZ'] == wz, 'IuK'])\n",
    "    jahresIKT = numpy.array([anwIKT/(365*96)] * (365*96))\n",
    "\n",
    "    dynFacIKT = iktJahr/numpy.amax(iktJahr)\n",
    "    if absDemand == True:\n",
    "        resIKT = iktJahr*(sum(jahresIKT)/sum(dynFacIKT))\n",
    "    else:\n",
    "        resIKT = dynFacIKT.copy()\n",
    "\n",
    "    iktTotal = numpy.stack((date, time, weekday, season, schedule, resIKT), axis= -1)\n",
    "    iktModel = Model(dummy, dummy, dummy, resIKT, iktTotal)\n",
    "    return iktModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prozesswärme-Modell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_processHeat_demand(wz, occModel, anwBil):\n",
    "    \"\"\"\n",
    "    Modellierung der Gleichzeitigkeitsfaktoren der Prozesswärme.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).\n",
    "    occModel : Model\n",
    "        Anwesenheitsmodell.\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    procHModel : Model\n",
    "        Instanz der Klasse \"Model\" mit Lastprofilen zur Prozesswärme [PJ].\n",
    "\n",
    "    \"\"\"\n",
    "    procHJahr = numpy.ones(35040).astype(numpy.float)\n",
    "    if wz == 47:\n",
    "        procHA = occModel.a.copy()\n",
    "        procHB = occModel.b.copy()\n",
    "        procHC = occModel.c.copy()\n",
    "        for lst in [procHA, procHB, procHC]:\n",
    "            for i in range(len(lst)):\n",
    "                if lst[i] > 0:  #APH_47.1\n",
    "                    lst[i] = 1\n",
    "                if lst[i] == 0:\n",
    "                    lst[i] = 0.2\n",
    "        \n",
    "\n",
    "        procHJahrList = []\n",
    "        for i in range(365):\n",
    "            if schedule[i*96] == 'a':\n",
    "                procHJahrList.extend(float(i) for i in procHA)\n",
    "            if schedule[i*96] == 'b':\n",
    "                procHJahrList.extend(float(i) for i in procHB)\n",
    "            if schedule[i*96] == 'c':\n",
    "                procHJahrList.extend(float(i) for i in procHC)\n",
    "        procHJahr = numpy.array(procHJahrList).astype(numpy.float)\n",
    "        \n",
    "    if wz == 55:\n",
    "        if ownAssumptions:\n",
    "            procH = numpy.ones(96)\n",
    "            procH = insert_assumption(procH, 0, 5, 0.2).copy()  # APH_55.1\n",
    "            procHJahrList = []\n",
    "            for i in range(365):\n",
    "                    procHJahrList.extend(float(i) for i in procH)\n",
    "            procHJahr = numpy.array(procHJahrList).astype(numpy.float)\n",
    "\n",
    "\n",
    "    anwprocH = float(anwBil.loc[anwBil['WZ'] == wz, 'Sonstige Prozesswärme'])\n",
    "    jahresProcH = numpy.array([anwprocH/(365*96)] * (365*96))\n",
    "\n",
    "    dynFacProcH = procHJahr/numpy.amax(procHJahr)\n",
    "    if absDemand == True:\n",
    "        resPH = procHJahr*(sum(jahresProcH)/sum(dynFacProcH))\n",
    "    else:\n",
    "        resPH = dynFacProcH.copy()\n",
    "\n",
    "    procHTotal = numpy.stack((date, time, weekday, season, schedule, resPH), axis= -1)\n",
    "    procHModel = Model(dummy,dummy,dummy, resPH, procHTotal)\n",
    "    return procHModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gesamtmodell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_energy_demand(wz_mod, factSia, factDin, factGS, anwBil, df, compare, wpshare):\n",
    "    \"\"\"\n",
    "    Übergreifende Funktion, die mit Angabe aller Parameter die Funktionen der Gleichzeitigkeitsfaktoren\n",
    "    aufruft und in ein gemeinsames Modell zusammenfügt.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    wz_mod : int\n",
    "        Ganzzahl des Wirtschaftszweigs (6471, 47, 55, 86, 85).    \n",
    "    factSia : float\n",
    "        Anteil zwischen SIA und ISO Normen, begrenzt auf Intervall [0,1]. (LambdaSIA)\n",
    "    factDin : float\n",
    "        Anteil der Minderung von Anwesenheiten außerhalb der DIN Norm, begrenzt auf Intervall [0,1].(LambdaDIN)\n",
    "    \n",
    "    factGS : float\n",
    "        Anteil des Einflusses der Solarstrahlung auf die Beleuchtung (LambdaGS), begrenzt auf dem Intervall [0,1].\n",
    "\n",
    "    anwBil : DataFrame\n",
    "        Anwendungsbilanz (Bspw. 2018 oder die Szenarien für 2035).\n",
    "    df : Boolean\n",
    "        True: DataFrame wird zurückgegeben. Falls: Nur der R^2-Score wird zurückgegeben\n",
    "    compare : Boolean\n",
    "        Entscheidet, ob ein Vergleich mit Branchenlastprofilen stattfinden soll.\n",
    "    wpshare : float\n",
    "        Anteil der Wärmepumpen an der Raumwärme im betrachteten Jahr.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float/DataFrame\n",
    "        Gibt entweder float \"Score\" zurück, oder das gesamte DataFrame.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    Mod_occ = model_occupancy_profile(wz_mod, factSia, factDin)\n",
    "    outDF = pd.DataFrame(data = Mod_occ.total, columns = [\"Date\", \"Time\", \"Wochentag\", \"Saison\", \"Tagtyp\", \"Anwesenheit\"])\n",
    "    outDF[\"Beleuchtung\"] = model_lighting_demand(wz_mod, Mod_occ, factGS, anwBil).jahr\n",
    "    outDF[\"Klimakälte\"] = model_ambient_cooling_demand(wz_mod, Mod_occ, anwBil).jahr\n",
    "    outDF[\"Prozesskälte\"] = model_process_cooling_demand(wz_mod, Mod_occ, anwBil).jahr\n",
    "    outDF[\"Raumwärme und Warmwasser\"] = model_space_heating_demand(wz_mod, Mod_occ, anwBil, wpshare).jahr\n",
    "    outDF[\"Mechanische Energie\"] = model_mechenergy_demand(wz_mod, Mod_occ, anwBil).jahr\n",
    "    outDF[\"IKT\"] = model_ikt_demand(wz_mod, Mod_occ, anwBil).jahr\n",
    "    outDF[\"Sonstige Prozesswärme\"] = model_processHeat_demand(wz_mod, Mod_occ, anwBil).jahr\n",
    "\n",
    "    # Alle QSTs summieren\n",
    "    gesamt = (outDF[\"Beleuchtung\"] + outDF[\"Klimakälte\"] + outDF[\"Prozesskälte\"] +\n",
    "              outDF[\"Raumwärme und Warmwasser\"] + outDF[\"Mechanische Energie\"] +\n",
    "              outDF[\"IKT\"] + outDF[\"Sonstige Prozesswärme\"])\n",
    "    outDF[\"GesamtPred\"] = gesamt\n",
    "\n",
    "    anwGes = anwBil.loc[anwBil['WZ'] == wz_mod, 'Gesamt'].values[0]\n",
    "\n",
    "    if compare:\n",
    "        outDF[\"Branchenlast\"] = dfBrLast[wz_mod].copy()\n",
    "\n",
    "        #Branchenlast an Anwendungsbilanzsumme skalieren\n",
    "        outDF[\"Branchenlast\"] = (outDF[\"Branchenlast\"]*\n",
    "                                 (1+(anwGes-outDF[\"Branchenlast\"].sum()) / outDF[\"Branchenlast\"].sum()))\n",
    "        apeOut = ape(outDF[\"Branchenlast\"].to_numpy(), outDF[\"GesamtPred\"].to_numpy())\n",
    "        outDF[\"APE\"] = apeOut\n",
    "        outDF = outDF.reset_index()\n",
    "        score_err = r2_score(outDF[\"Branchenlast\"].to_numpy(), outDF[\"GesamtPred\"].to_numpy())\n",
    "        outDF[\"R2\"] = score_err\n",
    "        score = numpy.array([score_err, factSia, factDin, factGS])\n",
    "        if df:\n",
    "            return outDF\n",
    "        else:\n",
    "            return score\n",
    "    else:\n",
    "        return outDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main: Lastgangsynthese\n",
    "\n",
    "Hauptmethode für das erste Modul der Arbeit: Branchen- und technologiespezifische Lastprofile.\n",
    "\n",
    "Erstellt einen Ausgabeordner und ordnet den Excel-Dateien ein Datum und einen Dateinamen je Modellschritt zu.\n",
    "    1. Modellierungsschritt: Bottom-up-Lastprofile  -- ohne Namenszusatz nach Datum\n",
    "    2. Modellierungsschritt: Lastprofile inkl. Annahmen -- Namenszusatz \"Annahmen\"\n",
    "    3. Modellierungsschritt: Lastprofile inkl. Annahmen und Hyperparameteroptimierung\n",
    "                                            -- Namenszusatz \"Annahmen_Opti\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not firstRun:\n",
    "    \"\"\"\n",
    "    Dateiname und -pfad wird erstellt.\n",
    "    \"\"\"\n",
    "    \n",
    "    now = datetime.now()\n",
    "    date_time = now.strftime(\"%d%m%Y-%H-%M-%S\")\n",
    "    outpath = outpathEnv + (\"/Results_\" + date_time)\n",
    "    bestParams = {}\n",
    "    if ownAssumptions:\n",
    "        outpath = outpath + str(\"_Annahmen\")\n",
    "    if doOptimize:\n",
    "        outpath = outpath + str(\"_Opti/\")\n",
    "    if ownAssumptions is True and doOptimize is False:\n",
    "        outpath = outpath + str(\"/\")\n",
    "    if ownAssumptions is False and doOptimize is False:\n",
    "        outpath = outpath + str(\"/\")\n",
    "\n",
    "    if not os.path.exists(outpath):\n",
    "        os.makedirs(outpath)\n",
    "    for wz_out in [6471, 55, 86, 85, 47]:\n",
    "        syntheseworking = True\n",
    "        if doOptimize:\n",
    "            \"\"\"\n",
    "            Hyperparameteroptimierung auf entsprechendem Grid\n",
    "            \"\"\"\n",
    "            param_grid = {'fSia': [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],\n",
    "                          'fDin': [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],\n",
    "                          'fGS': [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]}\n",
    "\n",
    "            grid = ParameterGrid(param_grid)\n",
    "\n",
    "            res = []\n",
    "            ct = 0\n",
    "            for params in grid:\n",
    "                res.append(model_energy_demand(wz_out, params['fSia'], params['fDin'], params['fGS'],\n",
    "                                               dfAnwend, False, True, 0.25))  # ASH_3 Anteil der Wärmepumpen 2018 bei 25%\n",
    "                perc = float(round((ct/1331)*100, 2))  # 11^3 = 1331 (Parametergrid)\n",
    "                x = \"Progress: \" + str(perc) + \"%\"\n",
    "                print(\"\\r\" + x, end=' ')\n",
    "                ct = ct+1\n",
    "            param_results = numpy.array(res)\n",
    "\n",
    "            index = numpy.where(param_results[:, 0] == numpy.amax(param_results[:, 0]))  # Maximum des R2\n",
    "\n",
    "            best_out = model_energy_demand(wz_out, float(param_results[index, 1]), float(param_results[index, 2]),\n",
    "                                           float(param_results[index, 3]), dfAnwend, True, True, 0.25)\n",
    "            print(param_results[index])\n",
    "            bestParams[wz_out] = param_results[index][0]\n",
    "\n",
    "        if doOptimize is False:\n",
    "            \"\"\"\n",
    "            Falls keine Optimierung stattfindet, werden die Default-Werte der Hyperparameter verwendet\n",
    "            \"\"\"\n",
    "            param_results = [defaultFactSia, defaultFactDin, defaultFactGS[wz_out]]\n",
    "            best_out = model_energy_demand(wz_out, param_results[0], param_results[1],\n",
    "                                           param_results[2], dfAnwend, True, True, 0.25)\n",
    "            bestParams[wz_out] = numpy.array(param_results)\n",
    "        if doOutput is True:\n",
    "\n",
    "            wz_out_str = str(wz_out)\n",
    "            flname = outpath + wz_out_str\n",
    "            if ownAssumptions:\n",
    "                flname = flname + str(\"_Annahmen\")\n",
    "\n",
    "            if doOptimize:\n",
    "                flname = flname + str(\"_Opti\")\n",
    "            if ownAssumptions is False and doOptimize is False:\n",
    "                flname = flname + str(\"_TrueBottomUp\")\n",
    "            score_err_str = str(int(best_out[\"R2\"][0]*100))\n",
    "            flname = flname + str(\"_R\" + str(score_err_str) + \".xlsx\")\n",
    "            writer = pd.ExcelWriter(flname, engine='xlsxwriter')\n",
    "            best_out.to_excel(writer, sheet_name='Sheet1')\n",
    "            if ownAssumptions:\n",
    "                paramDF = pd.DataFrame(data=param_results)\n",
    "                paramDF.to_excel(writer, sheet_name='Sheet2')\n",
    "            workbook = writer.book\n",
    "            workbook.close()\n",
    "    syntheseworking = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main: Szenario-Nachfrageflexi\n",
    "\n",
    "Hauptmethode für das zweite und dritte Modul der Arbeit: Fortschreibung der Lastprofile und\n",
    "Quantiizierung des Nachfrageflexibilisierungspotenzials.\n",
    "\n",
    "Fügt dem Ausgabeordner des ersten Moduls die Dateien je Szenario mit entsprechendem Namen hinzu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not firstRun:\n",
    "    while syntheseworking:\n",
    "        time.sleep(10)\n",
    "    else:\n",
    "        dsm_qst = ['Lüftung (Mechanische Energie)', 'Klimakälte',\n",
    "                   'Prozesskälte', 'Raumwärme und Warmwasser']\n",
    "        vent_jahr = numpy.tile(ventilation.copy(), 365)\n",
    "        for wz_out_scen in [6471, 55, 86, 85, 47]:\n",
    "            if doOptimize:\n",
    "                ref_scen = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][1], bestParams[wz_out_scen][2],    \n",
    "                                               bestParams[wz_out_scen][3], dfRefSzen, True, False, 0.58)\n",
    "                bas_scen = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][1], bestParams[wz_out_scen][2],\n",
    "                                               bestParams[wz_out_scen][3], dfBasSzen, True, False, 0.57)\n",
    "                status_quo = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][1], bestParams[wz_out_scen][2],\n",
    "                                               bestParams[wz_out_scen][3], dfAnwend, True, False, 0.57)\n",
    "                \n",
    "                # Siehe Anteile der Wärmepumpe an Raumwärme 2035 in Szenarien: St.: 58%, Norm.: 57%, Expl: 57%\n",
    "            if doOptimize is False:\n",
    "                ref_scen = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][0], bestParams[wz_out_scen][1],\n",
    "                                               bestParams[wz_out_scen][2], dfRefSzen, True, False, 0.58)\n",
    "                bas_scen = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][0], bestParams[wz_out_scen][1],\n",
    "                                               bestParams[wz_out_scen][2], dfBasSzen, True, False, 0.57)\n",
    "                status_quo = model_energy_demand(wz_out_scen, bestParams[wz_out_scen][0], bestParams[wz_out_scen][1],\n",
    "                                               bestParams[wz_out_scen][2], dfAnwend, True, False, 0.57)\n",
    "            ref_scen['Lüftung (Mechanische Energie)'] = ref_scen['Mechanische Energie']*vent_jahr*0.24\n",
    "            bas_scen['Lüftung (Mechanische Energie)'] = bas_scen['Mechanische Energie']*vent_jahr*0.24\n",
    "            status_quo['Lüftung (Mechanische Energie)'] = status_quo['Mechanische Energie']*vent_jahr*0.24\n",
    "            for sp in dsm_qst:\n",
    "                for scen in [ref_scen, bas_scen, status_quo]: # expl_scen]:\n",
    "                    scen[sp + \"_Pmax\"], scen[sp + \"_Pmin\"] = dsm_p(scen[sp].copy().to_numpy(), 1)\n",
    "\n",
    "                    if (sp == 'Lüftung (Mechanische Energie)' or sp == 'Klimakälte' or\n",
    "                            sp == 'Prozesskälte'):  # Hier wird eine Verschiebedauer deltaT = 1h verwendet\n",
    "                        scen[sp + \"_Emax\"], scen[sp + \"_Emin\"] = dsm_e(scen[sp].copy().to_numpy(), 1)\n",
    "                    if sp == 'Raumwärme und Warmwasser':    # Hier wird eine Verschiebedauer deltaT = 12h verwendet\n",
    "                        scen[sp + \"_Emax\"], scen[sp + \"_Emin\"] = dsm_e(scen[sp].copy().to_numpy(), 12)\n",
    "\n",
    "            ref_scen_flname = outpath + \"ReferenzstandsSzenario_\" + str(wz_out_scen) + \".xlsx\"\n",
    "            bas_scen_flname = outpath + \"BasisSzenario_\" + str(wz_out_scen) + \".xlsx\"\n",
    "            status_quo_flname = outpath + \"StatusquoSzenario_\" + str(wz_out_scen) + \".xlsx\"   \n",
    "            refwriter = pd.ExcelWriter(ref_scen_flname, engine='xlsxwriter')\n",
    "            baswriter = pd.ExcelWriter(bas_scen_flname, engine='xlsxwriter')\n",
    "            statusquowriter = pd.ExcelWriter(status_quo_flname, engine='xlsxwriter')\n",
    "            ref_scen.to_excel(refwriter, sheet_name=\"Sheet1\")\n",
    "            bas_scen.to_excel(baswriter, sheet_name=\"Sheet1\")\n",
    "            status_quo.to_excel(statusquowriter, sheet_name=\"Sheet1\")\n",
    "            workbook1 = refwriter.book\n",
    "            workbook2 = baswriter.book\n",
    "            workbook3 = statusquowriter.book\n",
    "            workbook1.close()\n",
    "            workbook2.close()\n",
    "            workbook3.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Variable firstRun wird hier verändert, sodass nach erstmaligem Import und \"Run All\" die Hauptmethoden aufgerufen werden können."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "firstRun = False"
   ]
  }
 ],
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