Intelligent grey forecasting model based on periodic aggregation generating operator and its application in forecasting clean energy

Accurate prediction of long-term and short-term clean energy production is the basis for understanding short-term clean energy supply capacity, long-term clean energy development trend and evaluating the effect of energy policies. However, under the circumstances of the large time span, the insufficient data samples and the periodic characteristics of seasonal clean energy production make the traditional grey prediction model prone to produce forecasting deviations. Given this situation, a novel seasonal fractional-order full-order time power discrete grey prediction model is initially proposed to deal with long-term clean energy production sequences featured with nonlinearity and periodicity. Based on the proposed model, we also propose a data-based algorithm to select the model structure adaptively. To prove the practicability of the new model for nonlinear long-term development trend, monthly periodic time series and quarterly periodic time series, this article uses the new model to predict annual hydropower capacity in North America, monthly natural gas production in China and quarterly solar power generation in China. And the prediction results are compared with the existing grey models and non-grey prediction models. Different methods including GM (1,1), DGM (1,1), NGM (1,1), ARGM (1,1), ENGM (1,1), Verhulst, CCRGM (1,1), FOTP-DGM(R) (1,1), PFSM (1,1), Holt-winters model, SARIMA model, SGM, HP-GM and DGGM are used as benchmarks. In experiments, the MAPE of the proposed model is 2.92%, 2.43%, and 7.87%, respectively. The results of empirical analysis indicate that the proposed model generally outperform the benchmark model as it can well capture nonlinear long-term development trend and seasonal characteristics.