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Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool
Author(s) -
Babatunde Akinola A.,
Abbasoglu Serkan
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.13098
Subject(s) - multicollinearity , variance inflation factor , statistics , linear regression , variables , regression analysis , reliability (semiconductor) , variance (accounting) , goodness of fit , linear model , econometrics , predictive modelling , computer science , mathematics , power (physics) , physics , accounting , quantum mechanics , business
The objective of this study is to develop a model that can predict the specific yield of PV plant and statistically examine the significance of independent variables during the process. The methods presented will help investors make quick and reliable decisions about installing PV plant at a particular site. To determine the best‐fit model for energy prediction, data from all variables were collected and a relevance check was conducted by examining the relationship between independent variables and dependent variable. The forecasting method makes use of Multiple Linear Regression model. Seven models were developed and two models with the best accuracy are proposed in this article. Variance Inflation Factor, which assesses potential multicollinearity among independent variables, was used to optimize the models that have similar characteristics. The first proposed model has coefficient of multiple determination of goodness of fit (R 2 ) of 96.8%. The second model has R 2 of 96%. The models were applied to data series selected from same site; prediction accuracies of 97% and 95% were obtained, respectively. They were also validated with Middle East Technical University North Cyprus Campus PV plant, yielding prediction accuracies of PV specific yield of 92% and 90%, respectively. It could be concluded that the results presented in this study will guide the energy experts about the reliability of the methods used to analyze the technical feasibility of medium or large scale PV projects. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13098, 2019