Power grid operation optimization and forecasting using a combined forecasting system

Abstract

Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub-predictor selection rule, and a multi-objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches.

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

“…A single model cannot achieve continuous satisfactory prediction performance when dealing with various data and is prone to ignoring the internal characteristics of the data or overfitting, which means most of them have limitations (Yu et al, 2021;Zhang et al, 2022). The hybrid model refers to the combination of multiple machine learning algorithms or models to achieve better performance and generalization ability.…”