基于多任务学习的智能建筑气温-负荷-价格联合预测OA

The joint prediction of temperature,load,and price is crucial for demand response and energy optimization in smart buildings.Due to environmental changes,fluctuations in energy demand,and uncertainties in pricing,single-task prediction methods struggle to fully capture the complex interdependencies among these factors,thereby limiting the accuracy and reliability of predictions.To address this issue,this paper proposes a multi-task joint prediction method for temperature,load,and price.First,the complex interrelationships among three uncertain variables—outdoor temperature,electrical load,and electricity price—are studied and verified.Then,a temporal convolutional network-weighted concatenation-bidirectional gated recurrent unit(TCN-WC-BiGRU)model is designed as a shared weight layer to extract and fuse multi-scale features.Finally,a pyramidal convolutional neural network-channel attention mechanism-bidirectional gated recurrent unit(PyCNN-CAM-BiGRU)model is constructed as a task-specific layer to achieve redundant noise suppression and deep information extraction.Experimental results show that,compared to various deep neural network models,the proposed method achieves a maximum reduction in mean absolute percentage error(MAPE)of 60.78%,49.96%,and 50.9%for temperature,load,and price prediction,respectively,as well as a maximum reduction in root mean squared error(RMSE)of 63.65%,47.98%,and 49.75%.