Here’s an updated version of your README.md in markdown format, including the additional explanations of the assumptions and international comparisons:

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Residential Energy Self-Sufficiency Analysis

This project performs an analysis of self-sufficiency for a typical UK semi-detached house by estimating energy demand from heating, hot water usage, household electricity, and electric vehicle (EV) charging, then comparing this demand with the energy generated from a solar PV system. The analysis helps identify how much of the household's energy demand can be met by solar power throughout the year and visualises key trends.

Project Overview

The goal of this analysis is to assess how self-sufficient a residential property can be using solar energy generation. Key elements analysed include:

• Solar energy generation from a PV system.
• Heating demand, which varies based on outdoor temperature and the performance of a heat pump.
• Hot water demand, with water heating modelled to take advantage of solar generation.
• Household electricity demand, modelled using a typical load profile with peaks in the morning and evening.
• EV charging demand, which is added to the household electricity demand during specified hours.

The project also calculates daily and weekly self-sufficiency, which represents the percentage of the total energy demand that can be met by solar generation.

Assumptions

We’ve made several assumptions in the analysis to simulate a realistic scenario for a typical UK household:

• Home Type: A standard semi-detached house with an area of 76 m², which is well-insulated to reduce heat loss.
• Solar PV System: The house is equipped with an 8 kW solar PV system installed on 40 m² of roof space.
• Hot Water Usage: Daily hot water consumption is 100 litres, heated by a heat pump with a Coefficient of Performance (COP) of 2.5.
• EV Charging: The household drives an electric vehicle for 10,000 miles/year, with an efficiency of 0.3 kWh/mile.
• Household Electricity: General household electricity use is set at 10 kWh/day.

International Comparisons

For comparison, we looked at solar potential and house sizes in other regions:

• UK 🇬🇧: With around 1,300–1,500 hours of sunshine per year, the UK isn’t known for its abundant solar resources. Still, with modern insulation and efficient solar panels, a semi-detached home (around 76 m²) can achieve respectable levels of self-sufficiency.

• Spain 🇪🇸: With 2,500–3,000 hours of sunshine per year and larger homes (average 97 m²), Spain offers significantly more potential for solar energy generation.

• Italy 🇮🇹: Italy enjoys 2,000–2,500 hours of sunshine per year and similar home sizes (average 81 m²), making it another country well-suited for residential solar self-sufficiency.

Key Features

• Energy Demand Estimation: Models energy demand for heating, hot water, household electricity, and EV charging, with daily and hourly breakdowns.
• Solar PV Generation: Estimates solar generation based on global horizontal irradiance (GHI), panel efficiency, and system losses.
• Self-Sufficiency Calculation: Computes the percentage of energy demand met by solar generation on a daily and weekly basis.
• Visualisation: Plots trends in self-sufficiency throughout the year, with annotations for key dates and an average self-sufficiency line.

Requirements

• Python 3.7+
• Pandas: For data manipulation and analysis.
• Matplotlib: For plotting and visualisation.
• Numpy: For numerical calculations.
• Plotly (optional): For interactive visualisations.

Running the Analysis

1. Data Input: The project requires a JSON file (weather_history.json) that contains historical weather data with fields for temperature, wind speed, and solar irradiance (GHI, DNI, DHI).

2. Run the Script: The main script performs the following tasks:

   • Loads and processes weather data.
   • Estimates solar power output from a PV system.
   • Calculates heating, hot water, household, and EV electricity demand.
   • Calculates daily and weekly self-sufficiency and visualises the results.

python energy_analysis.py

3. View Outputs: The script will generate and display plots showing daily and weekly self-sufficiency throughout the year, with important dates and the average self-sufficiency highlighted.

Customisation

• PV System Parameters: Modify the SOLAR_CAPACITY_KW, SOLAR_EFFICIENCY_FACTOR, and system_loss_factor in the code to adjust the solar generation model.
• Demand Profiles: Adjust HEAT_LOSS_KW_DEG_C, HOT_WATER_USAGE, and OTHER_ELECTRICITY_USAGE_DAY to simulate different household demand patterns.
• EV Charging: Change the vehicle's annual mileage (VEHICLE_ANNUAL_MILEAGE) or charging hours in estimate_other_demand to customise EV demand.

4. Correlation Analysis

Looking for patterns in correlation between daily solar and gross_demand. As analysed in the function calculate_daily_correlations:

1. For the warmer months Mar-Oct, the energy consumption of the house is pretty constant day to day (it doesn't need much heating). However, when it is sunnier the house does use less energy. This is likely because such days are likely to be warm and most houses also warm up a bit in the sunshine (called solar gain). Conversely, if we do get a particularly windy/wet spell in the Mar-Oct months that tends to make it colder. There's also much greater variation in solar variation day to day in the summer months (a cloudy/rainy day can have very little generation), and much less variation in energy consumption (there is never too much heating and most electricity demand is for non-heating uses like charging the EV).

2. For the harsher winter months Nov-Feb, the reverse is true. Energy consumption is much more variable (because some days are much colder than others), and higher in general. But sunnier winter days are also the days when the house consumes more energy. Why? Well winter days, even with with lots of sunshine rarely have enough sun to warm a house/air up, and they tend to coincide with high pressure, frosty nights. Another nice bonus is that solar panels are up to 10% more efficient at 0C than 20C, because they're not overheating.

Example Output

The script produces a plot of daily self-sufficiency throughout the year, with vertical lines indicating key dates (e.g. April 10, May 31, August 10), and a dashed horizontal line representing the average self-sufficiency. Here's an example output:

Contributions

Feel free to fork this repository, make improvements, and submit pull requests. Any contributions to enhance the functionality or add new features are welcome!

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This README.md gives potential users all the details they need to understand the assumptions, run the analysis, and interpret the results. If you'd like to tweak anything further, feel free to ask!