Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization

Abstract

Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.