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Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness

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  • Zator, Sławomir
  • Osuchowski, Jakub
  • Kasana, Singara Singh
  • Shelke, Nitin Arvind
  • Tomaszewski, Michał

Abstract

This article investigates the role of multi-oriented photovoltaic (PV) systems in enhancing energy self-sufficiency and reducing greenhouse gas emissions. Focusing on the evaluation of energy production prediction performance, the study utilizes data from four multi-oriented PV installations from 2022, standardized to 1 kWp capacity for comparative analysis. A custom-built measurement system, incorporating single-phase and three-phase meters, and solar irradiance data from the Solcast API, facilitated the creation of a dataset for predictive modeling. The Random Forest machine learning algorithm was employed to develop predictive models, enabling a comprehensive analysis of different installation orientations, including feature importance, prediction errors, and self-consumption and self-sufficiency rates. The study introduces new indicators alongside typical metrics to assess the accuracy of energy production predictions, aiming to address the surplus or deficit in energy forecasting. The results offer valuable insights into optimizing energy generation and grid integration of PV systems, highlighting the potential for improved prediction reliability and precision in multi-oriented installations.

Suggested Citation

  • Zator, Sławomir & Osuchowski, Jakub & Kasana, Singara Singh & Shelke, Nitin Arvind & Tomaszewski, Michał, 2025. "Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness," Renewable Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:renene:v:251:y:2025:i:c:s0960148125009875
    DOI: 10.1016/j.renene.2025.123325
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    References listed on IDEAS

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    1. Sławomir Zator & Waldemar Skomudek, 2020. "Impact of DSM on Energy Management in a Single-Family House with a Heat Pump and Photovoltaic Installation," Energies, MDPI, vol. 13(20), pages 1-20, October.
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