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

Abstract

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‐DGMr (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.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

“…Considering that the traditional grey forecasting model only considers the problem of integer order time delay, Ma et al proposed a new fractional-order time delay grey forecasting model to predict the gas consumption in Chongqing, China (Ma et al , 2019). Considering the adaptation problem of traditional time-varying parametric grey forecasting models due to their fixed structure (Qian and Sui, 2021; Sui and Qian, 2021b), Ding and Li et al proposed an adaptive discrete grey forecasting model with time-varying parameters (ATDGM (1,1)) based on the DGM (1,1) model, which further enhanced the ability of discrete grey forecasting models to capture nonlinear, periodic and volatile features of data series (Ding et al , 2021). Besides, Liu and Wu et al proposed a fractional grey model with time power term to forecast natural gas consumption based on discretization technique and fractional accumulation generation operator (Liu, Wu et al , 2021).…”

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

“…In addition, the floating structure suffers fatigue due to the wind, ocean waves and currents, which may also cause a decrease in energy production. Therefore, wind and wave forecasting models are essential to deployment, predict energy production, maximize power generation, and minimize the fatigue to which these marine turbines are exposed [7][8][9]. Moreover, these external disturbances present a significant challenge in FOWTs caused by the misalignment between wind and wave direction, which affects the efficiency of turbine control [10].…”