Stochastic Model for PV Sensor Array Data
Abstract
Recently, a number of researchers have investigated photovoltaic (PV) system modeling. Modelling a PV panel and its incident solar radiation to predict future trends improves a system's performance. This paper presents a fast, practical method that can be used to predict PV output power. By using present data of weather condition and present output power of the PV system, this predictor is modeled using linear regression analysis. The data from multiple sensors is collected only once before it is correlated to one sensor so that, in the future, only one sensor is needed to collect the data. Several experiments conducted under different weather conditions and different windows sizes of linear regression were completed to validate this method. These results were compared to the Meinel and Meinel model. This method yielded promising results, as the root mean square errors were low.
Recommended Citation
F. Alfaris et al., "Stochastic Model for PV Sensor Array Data," Proceedings of the 3rd International Conference on Renewable Energy Research and Applications (2014, Milwaukee, WI), pp. 798 - 803, Institute of Electrical and Electronics Engineers (IEEE), Oct 2014.
The definitive version is available at https://doi.org/10.1109/ICRERA.2014.7016495
Meeting Name
3rd International Conference on Renewable Energy Research and Applications (2014: Oct. 19-22, Milwaukee, WI)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Incident Solar Radiation; Mean Square Error; Meteorology; Photovoltaic Cells; Regression Analysis; Stochastic Systems; Sun; Future Trends; Multiple Sensors; Output Power; Photovoltaic Systems; Practical Method; PV System; Root Mean Square Errors; System's Performance; Stochastic Models; Mathematical Model; Data Models; Linear Regression; Arrays; Predictive Models; Correlation Coefficient; Stochastic Processes; Mean Square Error Methods; Photovoltaic Power Systems; Regression Analysis; Solar Radiation
International Standard Book Number (ISBN)
978-1479937950
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2014
Comments
This work was supported, in part, by the National Science Foundation under award ECCS-0900940.