Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings
Abstract
:1. Introduction
2. Case Study
3. Methods
3.1. Multilayer Perceptron (MLP)
3.2. Support Vector Regression (SVR)
4. Simulation and Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Mathematical Symbol | Variables |
---|---|
Relative compactness | |
Surface area | |
Wall area | |
Roof area | |
Overall height | |
Orientation | |
Glazing area | |
Glazing area distribution | |
Heating load | |
Cooling load |
Heating Load | Cooling Load | |||||||
---|---|---|---|---|---|---|---|---|
R | MSE | RMSE | MAE | R | MSE | RMSE | MAE | |
MLP | 0.9993 | 0.2335 | 0.4832 | 0.4118 | 0.9824 | 6.896 | 2.626 | 2.0973 |
SVR | 0.9979 | 0.7838 | 0.8853 | 0.7780 | 0.9878 | 3.024 | 1.7389 | 1.4762 |
Data Type | References | Heating Load (R) | Cooling Load (R) |
---|---|---|---|
Used data in this paper | MLP in this paper | 0.9993 | 0.9824 |
SVR in this paper | 0.9979 | 0.9878 | |
DNN [14] | 0.9805 | 0.9976 | |
GBM [14] | 0.9853 | 0.9853 | |
GPR [14] | 0.9984 | 0.9913 | |
MPMR [14] | 0.8802 | 0.8955 | |
ANN [15] | 0.9980 | 0.9840 | |
CART [15] | 0.9960 | 0.9810 | |
GLR [15] | 0.9950 | 0.9830 | |
CHAID [15] | 0.9950 | 0.9810 | |
GA-ANN [18] | 0.9800 | - | |
PSO-ANN [18] | 0.9720 | - | |
ICA-ANN [18] | 0.9700 | - | |
ABC-ANN [18] | 0.9730 | - | |
Different data | GRNN [28] | - | 0.9640 |
PENN [20] | - | 0.9500 | |
MLR [20] | - | 0.7510 | |
AR [20] | - | 0.8370 | |
ARX [20] | - | 08640 | |
MNR (initial prediction) [20] | - | 0.8990 | |
MNR (final calibration) [20] | - | 0.9580 | |
ANN [21] | 0.9900 | - | |
Decision tree [22] | 0.92 | - |
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Moradzadeh, A.; Mansour-Saatloo, A.; Mohammadi-Ivatloo, B.; Anvari-Moghaddam, A. Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings. Appl. Sci. 2020, 10, 3829. https://doi.org/10.3390/app10113829
Moradzadeh A, Mansour-Saatloo A, Mohammadi-Ivatloo B, Anvari-Moghaddam A. Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings. Applied Sciences. 2020; 10(11):3829. https://doi.org/10.3390/app10113829
Chicago/Turabian StyleMoradzadeh, Arash, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo, and Amjad Anvari-Moghaddam. 2020. "Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings" Applied Sciences 10, no. 11: 3829. https://doi.org/10.3390/app10113829