Integrated Energy System Load Forecasting Based on Multi-energy Demand Response and Improved BiLSTM

[Objective] With the trend of energy consumption diversification， multiload forecasting plays an increasingly important role in optimizing the scheduling and operation planning of integrated energy systems（IES）. [Methods] To address the problem in which the coupling relationship between multiple loads is often ignored in current integrated energy system load forecasting research， a multiple load joint forecasting method is proposed in this study for integrated energy systems based on the multi-energy demand response and improved bidirectional long short-term memory （BiLSTM）. First， by integrating user demand response behavior， the input feature variables of the multi-energy demand response is constructed， and together with multiload forecasting， strong correlation features selected by the maximum information coefficient form the input feature set of the prediction model. Second， the crested porcupine optimizer is improved based on the chaotic mapping theory and elite reverse learning strategy to optimize the model parameters of the BiLSTM neural network. Finally， based on the multihead self-attention mechanism， the input feature weight is adaptively adjusted. The simulation results show that the prediction accuracy of the proposed multiload joint forecasting method is significantly improved compared with the single-load forecasting method. [Results] Compared with the multiload forecasting method without considering the demand response， the mean absolute percentage error of the electricity， heat， and cooling loads was reduced by 6.59%， 13.04%， and 24.86%， respectively. In addition， compared with other forecasting models， the model proposed in this study is more effective in improving the prediction accuracy and can achieve more accurate multi-element load forecasting. [Conclusions] The proposed load forecasting method was combined with integrated energy system dispatching to analyze the economic benefits of load forecasting. Compared with ordinary dispatching， the total operating cost of the system using the proposed load forecasting method was reduced by 16.49%， which can improve the comprehensive benefits of integrated energy systems.