Short-term heating load prediction based on improved slime mould algorithm optimized BiLSTM
XUE Guijun
ZHAO Guanghao
SHI Caijuan
Abstract：Aiming at the problem of short-term heating load control prediction,a prediction model based on improved slime mould algorithm(ISMA)was proposed to optimize bidirectional long short term memory(BiLSTM).The slime mould algorithm was improved by using improved strategies such as cat mapping,T-distribution variation and stochastic reverse learning,and the improved slime mould algorithm optimized the parameters of BiLSTM network.The ISMA-BiLSTM model was constructed to predict the heating load of heat exchange station.Experimental results show that the prediction results of the as-proposed model are more reasonable and the prediction accuracy is improved to some extent,compared with SMA-BiLSTM,BiLSTM and LSTM models,so the ISMA-BiLSTM model can meet the needs of actual engineering control in short-term heating load prediction.
Keywords：central heating systemheat loadshort-term heating load control predictionslime mould algorithmbidirectional long short term memorycat mappingT-distribution variationstochastic reverse learning
Publication Date：2024-07-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 434-441 )