Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models

Abstract

In photovoltaic (PV) modules manufacturer provides rating under standard test conditions (STC). But STC hardly occur under outdoor conditions so it is important to investigate PV power by experimental analysis. In this study extensive literature survey of PV module electrical characteristics by conventional methods and ANN techniques are carried out. It is found that experimental analysis of PV modules maximum power under outdoor conditions remains a major research area. For this measurement of 75Wp PV module are performed under outdoor conditions at Centre for Energy and Environmental Engineering, National Institute of Technology, Hamirpur, India. To find most influencing variables for PV power prediction, five different sets of parameters are served as inputs to establish five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) Models which is novelty of this paper. The results shows that solar radiation and air temperature are found to be most influencing input variables for ANN based prediction of maximum power produced by PV module with mean absolute percentage (MAPE) of 2.15 %. The mean absolute percentage (MAPE) errors for ANN models are found to vary between 2.15 % to 2.55 % whereas for MLR models it varies from 13.04 % to 19.34 %, showing better prediction of ANN models.