Forecasting Solar Energy Consumption Using A Fractional Discrete Grey Model With Time Power Term
Abstract Accurate prediction of energy consumption is an important basis for policymakers to formulate and improve energy policies and measures. In this paper, a new grey prediction model FDGM(1,1, tα ) is proposed. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power α in the model. A numerical example and four sets of solar energy consumption data (France, South Korea, OECD, and Asia Pacific region) are used to establish the FDGM(1,1, tα ) model. Based on the idea of metabolism, the solar energy consumption of the above four economies in the next 10 years is predicted. The results show that the FDGM(1,1, tα ) model is more reliable and effective than the other seven grey models. From 2020 to 2029, the solar energy consumption in South Korea, the OECD, and the Asia Pacific region will gradually increase; the solar energy consumption in France will slowly increase in the next few years and will gradually decrease after reaching a peak in 2026. The grey prediction model FDGM(1,1, tα ) proposed in this paper has strong adaptability and can be used not only for the prediction of solar energy consumption but also for the prediction of other energy sources.