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.

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.

How is forecasting model of PV power generation based on historical time series data?

A significant number of historical time series data of PV output power and corresponding meteorological variables are used to establish the forecasting model of PV power generation. The historical time series data are normally divided into two groups: the training and testing data.

How can we predict PV production?

The ability to predict PV production is therefore an essential tool to capture economies in a market with a high penetration of non-predictable energy. Currently simulation models and meteorological forecasting resources for specific PV plants are well proven technologies.

Are regression techniques reliable for solar PV power generation?

Findings from literature suggests that regression techniques require low computational capabilities and produce accurate and reliable predictions of solar PV power generation when compared to other techniques [48, 29, 27, 28, 31, 26, 25].