PV power forecasting based on data-driven models: a review

Accurate PV power forecasting techniques are a prerequisite for the optimal management of the grid and its stability. This paper presents a review of the recent developments in the field of PV power forecasting, mainly focusing on the literature which uses ML techniques. The ML techniques (sub-branch of artificial intelligence) are extensively used due to their ability to solve nonlinear and complex data structures. PV power forecasting can either be direct, or indirect, which involves solar irradiance forecast model, plane of array irradiance estimation model, and PV performance model. This paper presents a review of both of these pathways of PV power forecasting based on the proposed methodology, forecast horizons and the considered input parameters. In case of unavailability of historical PV power for a new PV plant and in case of failure of real-time data acquisition, indirect PV power forecasting can be a viable alternative. Although the performance ranking of various ML models is complicated and no model is universal, recent studies suggest that methodologies like deep neural networks and ensemble or hybrid models outperform conventional methods for short-term PV forecasting. Recent articles also present the various intelligent optimisation and data-preparation techniques to improve performance accuracy.

Automated summary

Accurate PV power forecasting techniques are essential for grid management and stability, with a focus on machine learning methods. The forecasting can be direct or indirect, with indirect forecasting serving as an alternative in the absence of historical data or real-time data acquisition failure. Recent studies have shown that deep neural networks and ensemble models perform better in short-term forecasting.