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Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

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  • Ajith Gopi

    (Energy Sustainability Research Group, Automotive Engineering Center, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
    Agency for New and Renewable Energy Research and Technology (ANERT), Thiruvananthapuram 695033, India)

  • Prabhakar Sharma

    (School of Engineering Sciences, Delhi Skill and Entrepreneurship University, Delhi 110089, India)

  • Kumarasamy Sudhakar

    (Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
    Centre of Excellence for Advancement Research Fluid Flow (Fluid Center), Universiti Malaysia Pahang, Gambang 26300, Pahang, Malaysia
    Department of Electric Power Stations, Network and Supply Systems, South Ural State University (National Research University), 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

  • Wai Keng Ngui

    (Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia)

  • Irina Kirpichnikova

    (Department of Electric Power Stations, Network and Supply Systems, South Ural State University (National Research University), 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

  • Erdem Cuce

    (Department of Mechanical Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Zihni Derin Campus, 53100 Rize, Turkey)

Abstract

Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R 2 ), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R 2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.

Suggested Citation

  • Ajith Gopi & Prabhakar Sharma & Kumarasamy Sudhakar & Wai Keng Ngui & Irina Kirpichnikova & Erdem Cuce, 2022. "Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques," Sustainability, MDPI, vol. 15(1), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:439-:d:1016605
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    References listed on IDEAS

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    3. Bowen Zhou & Xinyu Chen & Guangdi Li & Peng Gu & Jing Huang & Bo Yang, 2023. "XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions," Sustainability, MDPI, vol. 15(17), pages 1-24, September.

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