Air Conditioning Load Forecast Based on LSTM Combining Improved Particle Swarm Algorithm and Attention Mechanism

Air-conditioning load forecasting is of great significance for saving building energy consumption and maintaining stable load operation of equipment, so research on air-conditioning load forecasting has high practical significance and social value. The building model is established, the air-conditioning load data is obtained by TRNSYS load simulation software, and the IPSO-AM-LSTM neural network model is established by Python. Use the IPSO algorithm to find the best parameters predicted by its model and make predictions. Several different neural network models were trained and validated on building air conditioning load data. Compared with the traditional LSTM model, the MAE, MAPE, and RMSE of IPSO-AM-LSTM are reduced by 61.9%, 46.4%, and 47.1% respectively.