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Disaggregating Longer-Term Trends from Seasonal Variations in Measured PV System Performance

Research output: Contribution to journalArticlepeer-review

Abstract

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into its constituent components, allowing us to find out the impact of the system degradation, maintenance, and weather variations from the inherent seasonal patterns. Our results reveal significant trends in PV system performance, indicating the need for proactive maintenance strategies to mitigate degradation effects. Moreover, we quantify the impact of changing weather patterns and provide recommendations for optimising the system’s efficiency based on seasonally varying conditions. Hence, this study not only advances our understanding of the intricate variations within PV system performance but also provides practical guidance for enhancing the sustainability and effectiveness of solar energy utilisation in both residential and commercial settings.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalElectricity
Volume5
Issue number1
DOIs
Publication statusPublished - Mar 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • long-term trends
  • measured data
  • performance analysis
  • photovoltaic systems
  • seasonal variations
  • trend disaggregation

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