A Novel Three-Parameter Grey Model with Background Value Optimization and Its Application in Energy Consumption Forecasting

Against the backdrop of sustained growth in energy demand and energy transformation in China, accurately predicting future energy consumption trends is essential to developing science-based energy strategies and ensuring energy security. Traditional grey models suffer from limited prediction accuracy due to irrational background value settings. To address this issue, we introduced a structural optimization by adjusting the parameter count within the background value and employed the Simpson formula to reconstruct it. We proposed a novel three-parameter background value grey model, designated as TPBSVGM(1,1). It utilized the annual consumption data of petroleum, natural gas, and primary electricity and other energy consumption from 2014 to 2023 to construct TPBSVGM(1,1) for energy consumption analysis. To assess the predictive accuracy of TPBSVGM(1,1), this study compared its performance with GM(1,1) and FGM(1,1) in two dimensions: the trends between predicted values and actual values, and error metrics. The results indicate that TPBSVGM(1,1) outperforms the comparative models in energy consumption forecasting. We further used the model to predict annual consumption of the three energy sources from 2024 to 2030, finding that total consumption continues to grow while growth rates decline to varying degrees. It provides reliable data support for China’s energy consumption regulation and energy structure optimization.