Solar photovoltaic generation forecasting methods: A review

Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand-alone networks, dramatic changes can be anticipated in both power system planning and operating stages. Solar photovoltaic integration requires the capability of handling the uncertainty and fluctuations of power output. In this case, solar photovoltaic power forecasting is a crucial aspect to ensure optimum planning and modelling of the solar photovoltaic plants. Accurate forecasting provides the grid operators and power system designers with significant information to design an optimal solar photovoltaic plant as well as managing the power of demand and supply. This paper presents an extensive review on recent advancements in the field of solar photovoltaic power forecasting. This paper aims to analyze and compare various methods of solar photovoltaic power forecasting in terms of characteristics and performance. This work classifies solar photovoltaic power forecasting methods into three major categories i.e., time-series statistical methods, physical methods, and ensemble methods. To date, Artificial Intelligence approaches are widely used due to their capability in solving the non-linear and complex structure of data. The performance analysis shows that these methods outperform the traditional methods. Recently, the ensemble methods were also developed by researchers to extract the unique features of single models to enhance the forecast model performances. This combination produces accurate results compared to individual models. This paper also elaborates on the metrics assessment which was implemented to evaluate the forecast model performances. This work provides information which is beneficial for researchers and engineers who are involved in the modelling and planning of the solar photovoltaic plant.