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Abstract <p>Degradation in solar photovoltaic (PV) systems impacts their overall efficiency as they can decline in power production over time. This study pioneers Indonesia’s specific approach to solar PV degradation forecasting, implementing advanced data-science techniques. It forecasts the output power of six solar PV systems in Java, Sumatra, and Kalimantan islands using historical time series data by considering degradation factors. The study uses web scraping techniques, statistical methods for data preprocessing, and machine learning algorithms for predicting future power outputs. The study forecasts 1.53–3.72% annual degradation rate with forecast accuracy ranging from 7.06 to 13.96%. The results emphasize the importance of factoring degradation into solar PV systems planning and operating in the South-East Asia’s tropical climates, the rapidly developing solar PV market.</p>

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Data-Driven Insights into Solar Photovoltaic Forecasted Performance and Trends in Indonesia

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • K. Kunaifi,
  • C. Alnuzipura,
  • A. Raghoebarsing

摘要

Abstract

Degradation in solar photovoltaic (PV) systems impacts their overall efficiency as they can decline in power production over time. This study pioneers Indonesia’s specific approach to solar PV degradation forecasting, implementing advanced data-science techniques. It forecasts the output power of six solar PV systems in Java, Sumatra, and Kalimantan islands using historical time series data by considering degradation factors. The study uses web scraping techniques, statistical methods for data preprocessing, and machine learning algorithms for predicting future power outputs. The study forecasts 1.53–3.72% annual degradation rate with forecast accuracy ranging from 7.06 to 13.96%. The results emphasize the importance of factoring degradation into solar PV systems planning and operating in the South-East Asia’s tropical climates, the rapidly developing solar PV market.