Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3829 |
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| author | Arash Moradzadeh Amin Mansour-Saatloo Behnam Mohammadi-Ivatloo Amjad Anvari-Moghaddam |
| author_facet | Arash Moradzadeh Amin Mansour-Saatloo Behnam Mohammadi-Ivatloo Amjad Anvari-Moghaddam |
| author_sort | Arash Moradzadeh |
| collection | DOAJ |
| description | Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies. |
| first_indexed | 2024-03-10T19:27:32Z |
| format | Article |
| id | doaj.art-3daeae40ac2e4e93abba84cbd8ea3575 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:27:32Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3daeae40ac2e4e93abba84cbd8ea35752023-11-20T02:23:09ZengMDPI AGApplied Sciences2076-34172020-05-011011382910.3390/app10113829Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential BuildingsArash Moradzadeh0Amin Mansour-Saatloo1Behnam Mohammadi-Ivatloo2Amjad Anvari-Moghaddam3Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranNowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.https://www.mdpi.com/2076-3417/10/11/3829energy managementload forecastingheating and coolingmachine learningmulti-layer perceptron (MLP)support vector regression (SVR) |
| spellingShingle | Arash Moradzadeh Amin Mansour-Saatloo Behnam Mohammadi-Ivatloo Amjad Anvari-Moghaddam Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings Applied Sciences energy management load forecasting heating and cooling machine learning multi-layer perceptron (MLP) support vector regression (SVR) |
| title | Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings |
| title_full | Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings |
| title_fullStr | Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings |
| title_full_unstemmed | Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings |
| title_short | Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings |
| title_sort | performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings |
| topic | energy management load forecasting heating and cooling machine learning multi-layer perceptron (MLP) support vector regression (SVR) |
| url | https://www.mdpi.com/2076-3417/10/11/3829 |
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