Effect of meteorological factors on photovoltaic power forecast based on the neural network

Wenbo Xiao,Jin Dai,Feng Cheng,Huaming Wu,Gina Nazario

doi:10.1039/c7ra10591f

Abstract

In this paper, the effects of meteorological factors (including air temperature, wind speed, and relative humidity) on photovoltaic (PV) power forecast using neural network models have been studied.

Highlights

The burning of fossil fuels, including coal, oil, and natural gas, causes pollution that can harm health, and foster climate change
The power output from PV power plants is in uenced by the weather conditions, so it has the shortcomings of intermittence and volatility
Photovoltaic power forecasting technologies have been widely studied in order to reduce the negative impacts of photovoltaic power on the existing grid.[3,4]

Summary

Introduction

The burning of fossil fuels, including coal, oil, and natural gas, causes pollution that can harm health, and foster climate change. The prediction accuracies of a neural network model are highly sensitive to the selection of input variables. The entrance layer has only two nodes (the irradiance intensity and the cell temperature) in some neural network models.[12,13] there are many factors, such as air temperature,[14] wind speed and relative humidity, that can affect the electrical power generation of the PV systems.[15] The selection of input factors is critical to identify the optimal function and increase the prediction accuracy in neural network models.[16]. The effects of meteorological factors including air temperature, wind speed, and relative humidity on the PV power prediction accuracy using neural network were investigated. The SSE/MSE and the coefficients of determination analysis showed that air temperature may be an important factor for predicting the output power of PV cells

Experimental setup

Neural network model

Results

Conclusion