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Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

Author

Listed:
  • Athanasios I. Salamanis

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Georgia Xanthopoulou

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Napoleon Bezas

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Christos Timplalexis

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Angelina D. Bintoudi

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Lampros Zyglakis

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Apostolos C. Tsolakis

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Dimosthenis Ioannidis

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Dionysios Kehagias

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

  • Dimitrios Tzovaras

    (Information Technologies Institute, Centre for Research and Technology–Hellas, P.O Box 60361, GR 57001 Thessaloniki, Greece)

Abstract

Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.

Suggested Citation

  • Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5978-:d:445829
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    Cited by:

    1. Jiaan Zhang & Yan Hao & Ruiqing Fan & Zhenzhen Wang, 2023. "An Ultra-Short-Term PV Power Forecasting Method for Changeable Weather Based on Clustering and Signal Decomposition," Energies, MDPI, vol. 16(7), pages 1-15, March.
    2. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    3. Nikolaos Kolokas & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization," Energies, MDPI, vol. 14(11), pages 1-36, May.
    4. Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Paschalis A. Gkaidatzis & Athanasios Tryferidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration," Energies, MDPI, vol. 14(10), pages 1-19, May.
    5. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.

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