Study on Short-Term Load Combination Forecasting Model Considering Historical Data Interval Construction

Abstract

In response to the insufficient accuracy of load forecasting in power system and the wide range of intervals, a combined short-term power load forecasting model considering the interval construction of historical data is proposed. First, the data are decomposed into relatively stable subsequences using extreme-point symmetric mode decomposition (ESMD), and the adaptive dispersion entropy (DE) of C–C algorithm is proposed to recombine similar subsequences. Then, periodicity and correlation analysis are used to determine the input set of each reconstructed component, and the hybrid strategy improved equilibrium optimizer (HSIEO) is proposed to optimize the output weights of the deep extreme learning machine (DELM) to obtain the prediction values of different components and the historical data errors, and the historical data intervals are constructed based on the errors of each component. Then, based on the upper and lower bound estimation method (LUBE), the proposed improved objective function is optimized using the HSIEO-DELM to obtain the component prediction intervals, and the optimal prediction intervals are obtained after superposition. Finally, the experimental comparison shows that the proposed algorithm has higher accuracy and better quality of model prediction intervals.