Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Amith Khandakar, Muhammad E. H. Chowdhury, Monzure- Khoda Kazi, Kamel Benhmed, Farid Touati, Mohammed Al-Hitmi and Antonio Jr S. P. Gonzales
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Amith Khandakar: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Muhammad E. H. Chowdhury: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Monzure- Khoda Kazi: Chemical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Kamel Benhmed: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Farid Touati: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Mohammed Al-Hitmi: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar
Antonio Jr S. P. Gonzales: Electrical Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar

Energies, 2019, vol. 12, issue 14, 1-19

Abstract: Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m 2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

Keywords: PV power prediction; artificial neural network; renewable energy; environmental parameters; multiple regression model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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