Model to Predict Heating and Cooling Load

Picture Source: Doğu İlmak

Data Set Information:

We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer. All the informations about data set referred from archive.ics.uci.edu.

Keywords

• Neural Networks
• Energy Efficiency
• Regression
• Computer Science
• Deep Learning

Attribute Information

The dataset contains eight attributes (or features, denoted by X1...X8) and two responses (or outcomes, denoted by y1 and y2). The aim is to use the eight features to predict each of the two responses.

1. X1 Relative Compactness
2. X2 Surface Area
3. X3 Wall Area
4. X4 Roof Area
5. X5 Overall Height
6. X6 Orientation
7. X7 Glazing Area
8. X8 Glazing Area Distribution
9. Output variables:
• y1 Heating Load
• y2 Cooling Load

Objectives

• Understand the data set & cleanup (data pre-processing).
• Build Multi-output model to predict heating and cooling load. Afterwards, evaluate the model.

Files

1. Heating and cooling loan prediction with multi-output model: energy_efficiency.ipynb. You can use the model last parameters with loading last_model.h5 or you can use the whole model here.

2. You can review model's loss and mean squared error values in each step: training.csv.

Main Graph

Relevant Papers

• A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012

Citation Request

• A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012


• For further details on the data analysis methodology: A. Tsanas, 'Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning', D.Phil. thesis, University of Oxford, 2012

References

1. Laurence Moroney
2. TensorFlow Tensorboard

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