Short-term Heating Load Prediction of Heat Exchange Station Based on DAIGA-LSTM Neural Network

Abstract

The heat exchange station is an essential part of the central heating system. In the actual heating system operation control, the short-term heating load prediction of the heat exchange station based on historical operation data plays an important role. In this paper, firstly, based on the Long Short-Term Memory (LSTM) neural network, a short-term heating load prediction model of heat exchange station is established. Secondly, because of the difficulty of adjusting parameters of the traditional LSTM neural network, a Dynamic Auxiliary Individual Genetic Algorithm (DAIGA) was proposed. Then, the primary hyperparameters of the prediction model were optimized by the proposed genetic algorithm to make the prediction performance of the model more accurate and stable. Finally, through comparison experiments with a variety of typical heating load prediction models, the proposed DAIGA-LSTM prediction model has strong applicability and good prediction performance.