Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

Abstract

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.