Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model
In the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined foreca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/11/2721 |
| _version_ | 1797459911629602816 |
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| author | Wenyu Wu Qinli Deng Xiaofang Shan Lei Miao Rui Wang Zhigang Ren |
| author_facet | Wenyu Wu Qinli Deng Xiaofang Shan Lei Miao Rui Wang Zhigang Ren |
| author_sort | Wenyu Wu |
| collection | DOAJ |
| description | In the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined forecasting model based on clustering results, which can achieve a short-time prediction of daily electricity based on a campus building’s electricity data over the past 15 days. Considering the diversity of campus buildings in energy consumption and functional aspects, the selected campus buildings are firstly classified into three categories using K-Means clustering in terms of their daily power consumption. Compared with the mainstream building energy consumption prediction models, i.e., LSTM and SVR, the results show that the combined forecast model is superior to other models. Furthermore, an average percentage fluctuation (APF) index is found to be close to the MAPE, which can reflect the prediction accuracy in advance. |
| first_indexed | 2024-03-09T16:57:42Z |
| format | Article |
| id | doaj.art-74026d138a8740d596f58f658d27dd41 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T16:57:42Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-74026d138a8740d596f58f658d27dd412023-11-24T14:33:07ZengMDPI AGBuildings2075-53092023-10-011311272110.3390/buildings13112721Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting ModelWenyu Wu0Qinli Deng1Xiaofang Shan2Lei Miao3Rui Wang4Zhigang Ren5School of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaLogistics Support Office, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, ChinaIn the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined forecasting model based on clustering results, which can achieve a short-time prediction of daily electricity based on a campus building’s electricity data over the past 15 days. Considering the diversity of campus buildings in energy consumption and functional aspects, the selected campus buildings are firstly classified into three categories using K-Means clustering in terms of their daily power consumption. Compared with the mainstream building energy consumption prediction models, i.e., LSTM and SVR, the results show that the combined forecast model is superior to other models. Furthermore, an average percentage fluctuation (APF) index is found to be close to the MAPE, which can reflect the prediction accuracy in advance.https://www.mdpi.com/2075-5309/13/11/2721time series predictioncampus buildingselectric consumptioncombined forecasting method |
| spellingShingle | Wenyu Wu Qinli Deng Xiaofang Shan Lei Miao Rui Wang Zhigang Ren Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model Buildings time series prediction campus buildings electric consumption combined forecasting method |
| title | Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model |
| title_full | Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model |
| title_fullStr | Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model |
| title_full_unstemmed | Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model |
| title_short | Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model |
| title_sort | short term forecasting of daily electricity of different campus building clusters based on a combined forecasting model |
| topic | time series prediction campus buildings electric consumption combined forecasting method |
| url | https://www.mdpi.com/2075-5309/13/11/2721 |
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