Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach
Accurate load forecasting is of vital importance for improving the energy utilization efficiency and economic profitability of intelligent buildings. However, load forecasting is restricted in the popularization and application of conventional load forecasting techniques due to the great difficulty...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/298 |
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| author | Lanqian Yang Jinmin Guo Huili Tian Min Liu Chang Huang Yang Cai |
| author_facet | Lanqian Yang Jinmin Guo Huili Tian Min Liu Chang Huang Yang Cai |
| author_sort | Lanqian Yang |
| collection | DOAJ |
| description | Accurate load forecasting is of vital importance for improving the energy utilization efficiency and economic profitability of intelligent buildings. However, load forecasting is restricted in the popularization and application of conventional load forecasting techniques due to the great difficulty in obtaining numerical weather prediction data at the hourly level and the requirement to conduct predictions on multiple time scales. Under the condition of lacking meteorological forecast data, this paper proposes to utilize a temporal convolutional network (TCN) to extract the coupled spatial features among multivariate loads. The reconstructed features are then input into the long short-term memory (LSTM) neural network to achieve the extraction of load time features. Subsequently, the self-attention mechanism is employed to strengthen the model’s ability to extract feature information. Finally, load forecasting is carried out through a fully connected network, and a multi-time scale prediction model for building multivariate loads based on TCN–LSTM–self-attention is constructed. Taking a hospital building as an example, this paper predicts the cooling, heating, and electrical loads of the hospital for the next 1 h, 1 day, and 1 week. The experimental results show that on multiple time scales, the TCN–LSTM–self-attention prediction model proposed in this paper is more accurate than the LSTM, CNN-LSTM, and TCN-LSTM models. Especially in the task of predicting cooling, heating, and electrical loads on a 1-week scale, the model proposed in this paper achieves improvements of 16.58%, 6.77%, and 3.87%, respectively, in the RMSE indicator compared with the TCN-LSTM model. |
| format | Article |
| id | doaj-art-3129b81c4e5f4e3aabbe86c5228da970 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-3129b81c4e5f4e3aabbe86c5228da9702025-01-24T13:26:31ZengMDPI AGBuildings2075-53092025-01-0115229810.3390/buildings15020298Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism ApproachLanqian Yang0Jinmin Guo1Huili Tian2Min Liu3Chang Huang4Yang Cai5Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou 510510, ChinaEnergy and Electricity Research Center, Jinan University, Zhuhai 519070, ChinaGuangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou 510510, ChinaEnergy and Electricity Research Center, Jinan University, Zhuhai 519070, ChinaEnergy and Electricity Research Center, Jinan University, Zhuhai 519070, ChinaEnergy and Electricity Research Center, Jinan University, Zhuhai 519070, ChinaAccurate load forecasting is of vital importance for improving the energy utilization efficiency and economic profitability of intelligent buildings. However, load forecasting is restricted in the popularization and application of conventional load forecasting techniques due to the great difficulty in obtaining numerical weather prediction data at the hourly level and the requirement to conduct predictions on multiple time scales. Under the condition of lacking meteorological forecast data, this paper proposes to utilize a temporal convolutional network (TCN) to extract the coupled spatial features among multivariate loads. The reconstructed features are then input into the long short-term memory (LSTM) neural network to achieve the extraction of load time features. Subsequently, the self-attention mechanism is employed to strengthen the model’s ability to extract feature information. Finally, load forecasting is carried out through a fully connected network, and a multi-time scale prediction model for building multivariate loads based on TCN–LSTM–self-attention is constructed. Taking a hospital building as an example, this paper predicts the cooling, heating, and electrical loads of the hospital for the next 1 h, 1 day, and 1 week. The experimental results show that on multiple time scales, the TCN–LSTM–self-attention prediction model proposed in this paper is more accurate than the LSTM, CNN-LSTM, and TCN-LSTM models. Especially in the task of predicting cooling, heating, and electrical loads on a 1-week scale, the model proposed in this paper achieves improvements of 16.58%, 6.77%, and 3.87%, respectively, in the RMSE indicator compared with the TCN-LSTM model.https://www.mdpi.com/2075-5309/15/2/298building load predictionmulti-time scalemulti-variable load scalemachine learning methodshybrid modeltemporal convolutional network |
| spellingShingle | Lanqian Yang Jinmin Guo Huili Tian Min Liu Chang Huang Yang Cai Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach Buildings building load prediction multi-time scale multi-variable load scale machine learning methods hybrid model temporal convolutional network |
| title | Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach |
| title_full | Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach |
| title_fullStr | Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach |
| title_full_unstemmed | Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach |
| title_short | Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Approach |
| title_sort | multi scale building load forecasting without relying on weather forecast data a temporal convolutional network long short term memory network and self attention mechanism approach |
| topic | building load prediction multi-time scale multi-variable load scale machine learning methods hybrid model temporal convolutional network |
| url | https://www.mdpi.com/2075-5309/15/2/298 |
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