Comparative Estimation of Electrical Characteristics of a Photovoltaic Module Using Regression and Artificial Neural Network Models

Abstract

Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage (I–V) characteristics of a photovoltaic module under varying operation conditions. In this paper, artificial neural network (ANN) models are compared with the regression models for five parameters of a single diode solar cell. In the configuration of the proposed PV models, the five parameters are predicted by regression and neural network models, and these parameters are put into an explicit expression such as the Lambert W function. The multivariate regression parameters are determined by using the least square method (LSM). The ANN model is constructed by using a four-layer, feed-forward neural network, in which the inputs are temperature and solar irradiance, and the outputs are the five parameters. By training an experimental dataset, the ANN model is built and utilized to predict the five parameters by reading the temperature and solar irradiance. The performance of the regression and ANN models is evaluated by using root mean squared error (RMSE) and mean absolute percentage error (MAPE). A comparative study of the regression and ANN models shows that the performance of the ANN models is better than the regression models.

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