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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2782-:d:249974
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    References listed on IDEAS

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    1. Zeki Ahmed Darwish & Hussein A. Kazem & K. Sopian & M. A. Alghoul & Hussain Alawadhi, 2018. "Experimental investigation of dust pollutants and the impact of environmental parameters on PV performance: an experimental study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(1), pages 155-174, February.
    2. Li, Jiaming & Ward, John K. & Tong, Jingnan & Collins, Lyle & Platt, Glenn, 2016. "Machine learning for solar irradiance forecasting of photovoltaic system," Renewable Energy, Elsevier, vol. 90(C), pages 542-553.
    3. Touati, Farid & Chowdhury, Noor Alam & Benhmed, Kamel & San Pedro Gonzales, Antonio J.R. & Al-Hitmi, Mohammed A. & Benammar, Mohieddine & Gastli, Adel & Ben-Brahim, Lazhar, 2017. "Long-term performance analysis and power prediction of PV technology in the State of Qatar," Renewable Energy, Elsevier, vol. 113(C), pages 952-965.
    4. Touati, Farid & Al-Hitmi, M.A. & Chowdhury, Noor Alam & Hamad, Jehan Abu & San Pedro Gonzales, Antonio J.R., 2016. "Investigation of solar PV performance under Doha weather using a customized measurement and monitoring system," Renewable Energy, Elsevier, vol. 89(C), pages 564-577.
    5. Nasser Ahmad & Amith Khandakar & Amir El-Tayeb & Kamel Benhmed & Atif Iqbal & Farid Touati, 2018. "Novel Design for Thermal Management of PV Cells in Harsh Environmental Conditions," Energies, MDPI, vol. 11(11), pages 1-9, November.
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    11. Jérémy Macaire & Sara Zermani & Laurent Linguet, 2023. "New Feature Selection Approach for Photovoltaïc Power Forecasting Using KCDE," Energies, MDPI, vol. 16(19), pages 1-13, September.
    12. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    13. Manel Marweni & Mansour Hajji & Majdi Mansouri & Mohamed Fouazi Mimouni, 2023. "Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques," Energies, MDPI, vol. 16(12), pages 1-16, June.
    14. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    15. Amr Zeedan & Abdulaziz Barakeh & Khaled Al-Fakhroo & Farid Touati & Antonio S. P. Gonzales, 2021. "Quantification of PV Power and Economic Losses Due to Soiling in Qatar," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    16. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    17. Mohamed Louzazni & Sameer Al-Dahidi & Marco Mussetta, 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique," Sustainability, MDPI, vol. 12(19), pages 1-23, October.
    18. Mahmoud Dhimish & Pavlos I. Lazaridis, 2022. "Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-16, November.
    19. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    20. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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