A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness.