Forecasting solar energy consumption using a fractional discrete grey model with time power term

In order to more accurately predict the global solar energy consumption, a new grey prediction model FDGM(1,1,\(t^{\alpha }\)) is proposed in this paper. The grey wolf optimizer (GWO) is used to optimize the fractional-order \(r\) and the time power \(\alpha\) in the model. The proposed FDGM(1,1,\(t^{\alpha }\)) model, two statistical models and other five existing grey models are used to simulate and predict the solar energy consumption in the four economies (France, South Korea, OECD and Asia Pacific region) from 2010 to 2029. The simulation results show that our proposed FDGM(1,1,\(t^{\alpha }\)) has higher accuracy than the other seven models. The prediction results based on FDGM(1,1,\(t^{\alpha }\)) show that in 2029, the solar energy consumption in South Korea, the OECD and the Asia Pacific region will reach 33,935.32 Ten-trillion J, 1,222,123.45 Ten-trillion J and 2,297,274.45 Ten-trillion J, respectively, but that in France will slowly increase in the next few years and will gradually decrease after reaching a peak of 14,727.34 Ten-trillion J in 2026. The better forecasting solar energy consumption by using the proposed model can provide useful information for the policy-makers to formulate and improve energy policies and measures.