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

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.

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Publication Details

Journal
Applied Solar Energy
Published
2025-04-01
DOI
https://doi.org/10.3103/s0003701x25600146
Citations
1
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.36
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article

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

1 citations
Applied Solar Energy
Photovoltaic System Optimization Techniques
0.36
article

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

article en
1 citations

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.

Applied Solar EnergyVol. 61(2)
Anton de Kom University of Suriname (SR), Universitas Batam (ID), Universitas Islam Negeri Sultan Syarif Kasim Riau (ID)
Climate action
Openalex Percentile: Top 35%
Photovoltaic System Optimization Techniques
0.36
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