Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model
Auteur(s): |
Wenyu Wu
Qinli Deng Xiaofang Shan Lei Miao Rui Wang Zhigang Ren |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 26 octobre 2023, n. 11, v. 13 |
Page(s): | 2721 |
DOI: | 10.3390/buildings13112721 |
Abstrait: |
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. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10753736 - Publié(e) le:
14.01.2024 - Modifié(e) le:
07.02.2024