# Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

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## Abstract

**:**

## 1. Introduction

## 2. Case Study

^{3}) with the same materials for all buildings. The newest and most common materials in the building construction industry were selected for each of the eighteen elements so that the materials used for each of these elements were the same for all forms of construction. In the design process, three types of glazing areas such as 10%, 25%, and 40% were used as percentages of the floor area. In addition, it was assumed that buildings were in Greece, Athens. Sixty percent humidity, 0.3 m/s wind speed, lightning level of 300 $1x$ and 0.6 clo of clothing were considered as internal design conditions during simulation, while the infiltration rate was set to 0.5 for the air change rate with a wind sensitivity of 0.25 air changer per hour. The dataset includes 768 samples with eight features for each sample, namely ${x}_{1},{x}_{2},\dots ,{x}_{8}$ and ${y}_{1},{y}_{2}$ as decision variables, which are listed in Table 1 [21,32]. This work aims to forecast ${y}_{1}$ as the heating load and ${y}_{2}$ as the cooling load using the aforementioned features as decision variables. Although the dataset was generated via simulation, it is notable that the proposed methods are applicable to the real-world dataset.

## 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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**Figure 3.**Correlation coefficient between the real value and the output of the MLP: (

**a**) heating load; (

**b**) cooling load.

**Figure 4.**Correlation coefficient between the real value and the output of the SVR; (

**a**) heating load; (

**b**) cooling load.

Mathematical Symbol | Variables |
---|---|

${x}_{1}$ | Relative compactness |

${x}_{2}$ | Surface area |

${x}_{3}$ | Wall area |

${x}_{4}$ | Roof area |

${x}_{5}$ | Overall height |

${x}_{6}$ | Orientation |

${x}_{7}$ | Glazing area |

${x}_{8}$ | Glazing area distribution |

${y}_{1}$ | Heating load |

${y}_{2}$ | 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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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Moradzadeh, 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