A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A load forecasting method for building air conditioning based on multi-stage attention mechanism
Load forecasting of building air conditioning is of great significance for improving the accuracy of power load forecasting of buildings and regional distribution networks. In order to improve the load forecasting accuracy of building air conditioning， a load forecasting method based on multi-stage attention mechanism is proposed. First， the attention module of influencing factors is constructed to fully consider the importance difference of different influencing factors for load forecasting of building air conditioning. Second， the LSTM network model is used to extract the implicit features of influencing factors in each hour. Finally， the temporal attention module is constructed to differentiate the implicit features of the influencing factors in different hours according to the importance of building air conditioning load forecasting to obtain the results of the air conditioning load forecasting. The example results show that the construction of the influencing factor attention module and the temporal attention module are both conducive to improving the model’s ability to fit the building air-conditioning load， thus effectively improving the load prediction accuracy of building air-conditioning.
A load forecasting method for building air conditioning based on multi-stage attention mechanism
Load forecasting of building air conditioning is of great significance for improving the accuracy of power load forecasting of buildings and regional distribution networks. In order to improve the load forecasting accuracy of building air conditioning， a load forecasting method based on multi-stage attention mechanism is proposed. First， the attention module of influencing factors is constructed to fully consider the importance difference of different influencing factors for load forecasting of building air conditioning. Second， the LSTM network model is used to extract the implicit features of influencing factors in each hour. Finally， the temporal attention module is constructed to differentiate the implicit features of the influencing factors in different hours according to the importance of building air conditioning load forecasting to obtain the results of the air conditioning load forecasting. The example results show that the construction of the influencing factor attention module and the temporal attention module are both conducive to improving the model’s ability to fit the building air-conditioning load， thus effectively improving the load prediction accuracy of building air-conditioning.
A load forecasting method for building air conditioning based on multi-stage attention mechanism
CHEN Donghai (author) / MA Xu (author) / WANG Bo (author) / ZHU Xiaojie (author) / BAI Wenbo (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model
| DOAJ | 2022
TBM Cutterhead Load Prediction Model Based on the Two-Stage Attention Mechanism
| Springer Verlag | 2024
A Hybrid Residential Short-Term Load Forecasting Method Using Attention Mechanism and Deep Learning
| DOAJ | 2022
The Software of Air-Conditioning Dynamic Load Simulation and Forecasting for the Walls of Building
| Trans Tech Publications | 2014
The Software of Air-Conditioning Dynamic Load Simulation and Forecasting for the Walls of Building
| British Library Conference Proceedings | 2014