Volume 46: Energy Transitions toward Carbon Neutrality: Part IX

Weather Classification for PV System and its Validation in Real-world Conditions Mingkun Jiang, Wenqian Zhang, Xuehong Li, Qi Yang, Ruoting Yang, Xiaofeng Li



The integration of photovoltaic systems into the power grid is becoming increasingly important for sustainable energy production. The efficiency of PV systems is heavily dependent on weather conditions, making accurate weather classification crucial for system performance prediction and optimization. In this study, we utilize a threshold-based weather classification method for PV systems and compare it to K-means clustering classification. The effectiveness of both methods was evaluated using real-world data from a PV system in a temperate climate region. This comparative analysis highlights the strengths and limitations of each approach, demonstrating their combined utility in enhancing the accuracy and reliability of weather classification, thereby offering a comprehensive tool for optimizing PV system performance across diverse meteorological conditions.

Keywords weather classification, threshold-based classification, photovoltaic output prediction, k-means clustering

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