Short-term Power Load Forecasting Method Based on HPO-LSTM Model

The flexible power dispatch and balance of supply and demand is essential for a stable and efficient power system. To achieve such goal, it is important to accurately forecast short- term power loads. A method for short-term power load forecasting using a combination of the hunter-prey optimizer (HPO) algorithm and the long short-term memory (LSTM) model is proposed for the first time, known as the HPO-LSTM forecasting model. The proposed HPO-LSTM model uses the random search HPO algorithm to optimize the parameters of the LSTM model, which avoids uncertainty of manual and empirical adj ustment of parameters, thus improving the forecasting accuracy and effectiveness. The proposed model was validated through experiments, which showed that it outperformed other comparative methods in accurately forecasting short-term power loads. The results demonstrate that the HPO-LSTM model has certain potential positive engineering application value of better planning and allocating resources to ensure reliable and efficient power system.