Photovoltaic panel power generation forecast

What are forecasting models for PV power generation?

A good number of research has been conducted to develop appropriate forecasting models in forecasting PV power generation with the targets of higher accuracy and minimum complexity with computational cost. These forecasting models are broadly classified into two categories: indirect and direct forecasting models.

How accurate is the forecasting of PV power generation?

Therefore, the accurate forecasting of PV power generation is considerably difficult. The inability to predict PV output power significantly affects its stability, dependability, and scheduling of the power system operation, not to mention the economic benefit [2, 3, 4].

Is there a framework for solar PV power generation prediction?

This review has outlined a pioneering, comprehensive framework for solar PV power generation prediction, addressing a critical need due to the intermittent and stochastic nature of RESs. This systematic framework integrates a structured three-phase approach with seven detailed modules, each addressing essential aspects of the prediction process.

Why is forecasting of photovoltaic power generation important?

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations.

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 do current photovoltaic power generation forecasting methods work?

Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation.