Estimating the power generating of a stand-alone solar photovoltaic panel using artificial neural networks and statistical methods

ABSTRACT

This study focuses on the effect of environmental factors on the power generation in a solar photovoltaic (PV) panel and the estimation of the power generated from this panel. Artificial Neural Network (ANN) and Multiple Linear Regression (MLR), as two different data-driven models, were used to assess the power of solar PV panels. Six factors are determined as input parameters in these models such as irradiance (W/m²), panel temperature (°C), ambient temperature (°C), wind speed (m/s), wind chill (°C), and humidity data (RH%). The output parameter of these models is the electrical power (W) generated instantly by the PV panel. In the development of both models, 550 experimental data sets (6x550 input data and 1 × 550 output data) were used, which were obtained from the position of the PV panel at different times throughout the year. In the light of these data, eight different training algorithms of ANN algorithms were tested and their success results were compared. According to the study results, the R² value was found 98.9% in ANN trained with Levenberg-Marquardt algorithm (trainlm), which showed the highest success. In the MLR analysis, the solar panel’s power estimation was found to be 94.8% (R²). Also, the weight of each environmental factor on the output power was determined by MLR analysis. The results indicate that the ANN model can successfully estimate the power generated under variable environmental conditions without extract a solar PV panel’s model-physical parameters. Also, the effects of environmental factors on power were determined by correlation and MLR analysis. When the presented methodology is generalized for PV plants, accurate energy planning can be made and a relationship can be established between demand and installed power capacity that prevents unrequited investment.

KEYWORDS:

• Renewable Energy
• Artificial Neural Network (ANN)
• Multiple Linear Regression (MLR)
• power estimation
• photovoltaic panel

Additional information

Notes on contributors

Mehtap Ayan

Mehtap I. Ayan received her master’s degree in Energy Systems Engineering from the Kirklareli University, Turkey in 2018. Currently, she is a Lecturer of Kirklareli University in Turkey. Her research interests include renewable energy systems and artificial intelligent algorithms

Hayrettin Toylan

Hayrettin Toylan received his Ph.D. degree in Machine Engineering from the Trakya University, Turkey in 2012. He became an Assistant Professor with the Department of Mechatronics Engineering at the Faculty of Technology, University of Kirklareli. He developed in the artificial intelligent algorithms for energy applications