Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks
Abstract
:1. Introduction
2. Presentation of the Social Housing Stock
3. The Use of the ANN Approach for Energy Expenses Modeling
3.1. The Presentation of the ANN Approach
3.2. Determination of the ANN Architecture
- the training set, which is used to train the ANN model and to adjust the connection weights;
- the testing set, which measures the ability of the model to be generalized; and
- the holdout set, which is used to determine the performances of the ANN model on patterns, which were not used in the previous two phases.
3.3. Analysis of the Influence of the Input Indicators on the Heating Expenses
4. The Use of the ANN Model for Establishing Renovation Strategy
5. Conclusions
Author Contribution
Conflicts of Interest
References
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Physical Indicators | Socio-Economic Indicators |
---|---|
Energy Performance Diagnostic (DPE) | Tenant age |
Dwelling area | Family size |
Building age | Tenant income |
Number of floors |
Model | % of Training | % of Testing | % of Holdout | R2 |
---|---|---|---|---|
1 | 50 | 25 | 25 | 0.71 |
2 | 70 | 30 | 0 | 0.58 |
3 | 100 | 0 | 0 | 0.72 |
4 | 66 | 26 | 8 | 0.67 |
5 | 74 | 18 | 8 | 0.74 |
Model | No. of Hidden Layers | No. of Nodes | Sum of Squares Error for Training | Sum of Squares Error for Testing | Relative Error for Training | Relative Error for Testing | R2 |
---|---|---|---|---|---|---|---|
B1 | 1 | 3 | 14.23 | 1.14 | 0.467 | 0.134 | 0.62 |
B2 | 1 | 5 | 9.16 | 1.58 | 0.30 | 0.186 | 0.74 |
B3 | 1 | 6 | 9.94 | 1.76 | 0.326 | 0.206 | 0.71 |
B4 | 2 | 4 | 16.39 | 2.42 | 0.537 | 0.284 | 0.54 |
Parameter | Weight |
---|---|
Building age | 0.23 |
DPE | 0.26 |
Number of floors | −0.24 |
Area | −0.58 |
Tenant age | −0.22 |
Family size | 0.32 |
Tenant income | 0.07 |
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Zabada, S.; Shahrour, I. Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks. Energies 2017, 10, 2086. https://doi.org/10.3390/en10122086
Zabada S, Shahrour I. Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks. Energies. 2017; 10(12):2086. https://doi.org/10.3390/en10122086
Chicago/Turabian StyleZabada, Shaker, and Isam Shahrour. 2017. "Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks" Energies 10, no. 12: 2086. https://doi.org/10.3390/en10122086