Stochastic Model for PV Sensor Array Data


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.

Meeting Name

3rd International Conference on Renewable Energy Research and Applications (2014: Oct. 19-22, Milwaukee, WI)


Electrical and Computer Engineering


This work was supported, in part, by the National Science Foundation under award ECCS-0900940.

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)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Oct 2014