A novel fuzzy rough set based long short-term memory integration model for energy consumption prediction of public buildings
| Autor: | Fengyu Zhou, Lanqiang Niu, Hongchang Sun, Yadong wang, Heng Li |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Mathematical optimization Long short term memory Artificial Intelligence Computer science 020209 energy 020208 electrical & electronic engineering 0202 electrical engineering electronic engineering information engineering General Engineering 02 engineering and technology Energy consumption Fuzzy rough sets |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 40:5715-5729 |
| ISSN: | 1875-8967 1064-1246 |
| Popis: | Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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