Document Type
Article
Publication Date
2017
Digital Object Identifier (DOI)
https://doi.org/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. The research is based on PV power data collected at Nanchang, China. Our results showed that prediction results of three neural network models were overall close to the experimental data. It indicated the accuracy of the neural network approach. The time–power curves showed that the prediction errors were relatively large for some time frames, especially at dusk. The SSE/MSE and the coefficients of determination analysis showed that the model including air temperature had the strongest correlation with experimental data than another 2 models including wind speed and relative humidity, which proves that air temperature is an important factor for predicting the output power of PV cells.
Rights Information
This work is licensed under a Creative Commons Attribution 3.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
RSC Advances, v. 7, issue 88, p. 55846-55850
Scholar Commons Citation
Xiao, Wenbo; Dai, Jin; Wu, Huaming; Nazario, Gina; and Cheng, Feng, "Effect of Meteorological Factors on Photovoltaic Power Forecast Based on the Neural Network" (2017). Pharmacy Faculty Publications. 91.
https://digitalcommons.usf.edu/pharm_facpub/91
