A review of the state of the art in solar photovoltaic output power forecasting using data-driven models

The integration of Photovoltaic (PV) systems into grid has a detrimental effect on grid stability, dependability, reliability, efficiency, economy, planning and scheduling. Thus, a reliable PV output prediction is necessary for grid stability. This paper presents a detailed review on PV power forecasting technique. A detailed evaluation of forecasting techniques reveals that solar irradiance is highly correlated with PV output which makes it mandatory to classify the weather as well as analyze the cloudy motion. The researchers in literature dominantly preferred very short term and short-term horizon for PV forecasting. Different PV forecasting models categorized into physical, persistence, statistical, machine learning and hybrid model used in literature are studied and evaluated using various performance metrics such as root mean square error and mean absolute error for accuracy. A thorough comparison of various forecasting techniques suggests that deep neural networks with ensemble technique or hybrid techniques supersedes the traditional approaches of PV power forecasting in terms of efficiency and accuracy.