ISSA-LSTM: A new data-driven method of heat load forecasting for building air conditioning

Abstract

Building air conditioning heat load is a critical demand response resource, and its forecasting serves as a fundamental basis for optimizing building energy consumption control. The existing air conditioning load forecasting model suffers from reduced accuracy and stability due to the significant influence of temperature, humidity, and other factors. This paper proposes a method for building air conditioning heat load forecasting using Long Short-Term Memory neural network (LSTM) optimized by the Improved Sparrow Search Algorithm (ISSA). First, an experimental system for data collection on building air conditioning thermal load is constructed to form a dataset. Subsequently, the Latin Hypercube Sampling (LHS) is introduced to improve the SSA and iteratively optimize the hyperparameters of the LSTM model using ISSA. Finally, different optimization algorithms including Particle Swarm Optimizer (PSO), Crow Search Algorithm (CSA) and Spider Wasp Optimizer (SWO) were developed to optimize the LSTM model to achieve similar purposes, using the coefficient of determination as an indicator for evaluating model accuracy. The results show that the proposed data-driven new method is the most accurate model for forecasting building air conditioning heat load in this study. The R2 of the ISSA-LSTM forecasting model is as high as 0.9971, and compared with the RNN, LSTM, GRU, Bi-LSTM, SWO-LSTM, and SSA-LSTM models, the RMSE of this model in air conditioning heat load forecasting is reduced by 80.9670%, 71.7390%, 87.8040%, 88.3027%, 86.1179%, and 47.3089%, respectively. Employing the ISSA-LSTM method significantly enhances precision in building air conditioning load forecasting and holds promising applications in optimizing building energy consumption control.