Research on Short-Term Power Load Forecasting Based on Hybrid Deep Learning

Aiming to boost the precision of short-term power load forecasts, a novel short-term power load forecasting model is suggested. This model incorporates hybrid deep learning techniques, taking into account the complex, random, and nonlinear nature of power load time series. Firstly, in order to speed up the convergence of the model, Bayesian optimization algorithm is used to optimize the parameters in XGBoost model. Then, in order to tackle the challenges posed by inaccurate forecasts and limited capacity for making generalized predictions in power load forecasting, a short-term power load forecasting model has been built using LSTM-XGBoost. The experimental comparison shows that the LSTM-XGBoost model optimized by Bayes has improved the error index compared with the traditional PSO-BP model and LSTM model. The findings provide solid evidence that the model significantly enhances the precision of load forecasting, making it a reliable tool for short-term power load predictions.