Photovoltaic power generation support method

What are the different solar PV output power forecasting methods?

We will consider some selected solar PV output power forecasting methods in this section. These methods include persistence, statistical, machine learning, and hybrid approaches. The persistence model involves the use of the solar PV output of the previous day at the same time.

Why is forecasting PV power generation important?

Accurately forecasting PV power generation can reduce the effect of PV power uncertainty on the grid, improve system reliability, maintain power quality, and increase the penetration level of PV systems.

Which method is used in forecasting PV power generation?

ANN is the most effective method and is popular among researchers since 1980. This method has been used in different prediction applications, including the forecasting of PV power generation with higher level of success. ANN is widely used in forecasting the PV power generation in most research because of non-linearity in meteorological data.

How to forecast PV power generation?

In this method, only the historical PV power output data are required to forecast the PV power generation. Generally, this model is used as a benchmark model. In the statistical methods, the PV power generation is forecasted by the statistical analysis of the different input variables. Therefore, the past time-series data are used in these methods.

Does solar PV power forecasting have a data-driven approach?

This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented.

Can solar PV power forecasting be improved?

The common forecasting techniques found in both the wind and solar literature were highlighted, best practices for forecasting evaluation were outlined, and areas for improvement were identified. Other studies, such as that of Gupta and Singh , have reviewed recent developments in solar PV power forecasting.