Forecasting China's hydropower generation using a novel seasonal optimized multivariate grey model

Abstract

Global warming and environmental degradation are essential issues that endanger human survival. The conflict between rising carbon emissions and carbon neutrality goals has prompted an urgent need for China's energy sector to step up efforts to develop clean energy generation. As a significant hydropower country, hydropower generation is China's mainstay of clean energy generation. The work contributing to sustainable hydropower development requires reasonable forecasts of clean energy generation. This paper proposes a seasonal optimized multivariate grey model that optimizes background value and supplements dummy variables to explore hidden factors through an optimization algorithm to the related sequences. The novel model improves the fitting and prediction accuracy through the loop supplementation of dummy variables. The model tests the effect of China's hydropower generation prediction and compares results with other methods. The mean absolute percentage error of the model training and test groups is 3.87 % and 0.83 %. Finally, this paper predicts hydropower generation in China from 2022 to 2025 based on the power generation during China's 13th Five-Year Plan Period.