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<p>Conventional solar energy forecasting relies on linear conversion models that assume direct proportional relationships between solar irradiation and energy output. This study examines operational data from the Bui solar power plant in Ghana to investigate non-linear characteristics in photovoltaic conversion efficiency. Analysis of 339 daily observations highlights operational regimes separated by threshold points at 2000 Wh/m² and 6000 Wh/m². Conversion efficiency decreases significantly from 53.53 MWh/kWh/m² at low irradiation to 42.58 MWh/kWh/m² at very high irradiation, representing a 20.5% reduction. Temperature sensitivity demonstrates complex interactions with irradiation levels, increasing from − 0.213 efficiency units per °C at low irradiation to -0.721 units per °C at very high irradiation, a 238.5% increase in thermal sensitivity. Seasonal analysis reveals statistically significant monthly efficiency variations ranging from + 4.2% (April) to -4.3% (May) that persist after controlling for irradiation and temperature (<i>p</i> &lt; 0.001). An adaptive modeling framework incorporating threshold-based segmentation, temperature compensation, and seasonal calibration reduces prediction errors by 48.6% compared to conventional linear approaches, decreasing mean absolute percentage error from 6.97% to 3.58%. The framework achieves forecast error reductions of up to 60.3% during extreme conditions. These findings demonstrate that utility-scale photovoltaic systems exhibit systematic non-linear behaviors that significantly impact forecasting accuracy, with implications for grid integration, operational optimization, and energy trading in renewable energy systems.</p>

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Adaptive solar energy modeling for sustainable urban infrastructure: addressing Non-Linear conversion challenges

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Sidique Gawusu,
  • Xiaobing Zhang,
  • Seidu Abdulai Jamatutu,
  • Emmanuel Kyei Yeboah,
  • Marizuk Waris Tizumah,
  • Sufyan Yakubu

摘要

Conventional solar energy forecasting relies on linear conversion models that assume direct proportional relationships between solar irradiation and energy output. This study examines operational data from the Bui solar power plant in Ghana to investigate non-linear characteristics in photovoltaic conversion efficiency. Analysis of 339 daily observations highlights operational regimes separated by threshold points at 2000 Wh/m² and 6000 Wh/m². Conversion efficiency decreases significantly from 53.53 MWh/kWh/m² at low irradiation to 42.58 MWh/kWh/m² at very high irradiation, representing a 20.5% reduction. Temperature sensitivity demonstrates complex interactions with irradiation levels, increasing from − 0.213 efficiency units per °C at low irradiation to -0.721 units per °C at very high irradiation, a 238.5% increase in thermal sensitivity. Seasonal analysis reveals statistically significant monthly efficiency variations ranging from + 4.2% (April) to -4.3% (May) that persist after controlling for irradiation and temperature (p < 0.001). An adaptive modeling framework incorporating threshold-based segmentation, temperature compensation, and seasonal calibration reduces prediction errors by 48.6% compared to conventional linear approaches, decreasing mean absolute percentage error from 6.97% to 3.58%. The framework achieves forecast error reductions of up to 60.3% during extreme conditions. These findings demonstrate that utility-scale photovoltaic systems exhibit systematic non-linear behaviors that significantly impact forecasting accuracy, with implications for grid integration, operational optimization, and energy trading in renewable energy systems.