An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization

During an era of rapid growth in electricity demand throughout society, accurate forecasting of electricity loads has become increasingly important to guarantee a stable power supply. Nevertheless, historical models do not address the structure of the data itself, and a single model cannot accurately determine the nonlinear characteristics of the data. This would not allow for accurate and stable predictions. With the aim of filling this gap, this paper proposes an innovative intelligent power load point-interval forecasting system. The system discretizes the time series, then performs efficient dimensionality reduction by fuzzification, and multi-level optimization of five benchmark deep learning models by the proposed multi-objective optimization algorithm, and finally analyzes the uncertainty of the prediction results. Experiments comparing the developed prediction system with other models were conducted on three datasets, and the prediction results were discussed for validation from multiple perspectives. The simulation results show that the proposed model has superior prediction accuracy, robustness and uncertainty analysis capability, and can provide accurate deterministic prediction information and fluctuation interval analysis to ensure the long-term safety and stability and operation of the grid.