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Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions

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  • Kristijan Brecl

    (Laboratory of Photovoltaics and Optoelectronics—LPVO, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

  • Marko Topič

    (Laboratory of Photovoltaics and Optoelectronics—LPVO, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

Abstract

When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s). While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertainty of the results is relatively high. However, in the presented approach, irradiance is calculated from discrete weather conditions and with correlation of forecasted meteorological data, an RMS error of 65%, and a R 2 correlation factor of 0.85 is feasible.

Suggested Citation

  • Kristijan Brecl & Marko Topič, 2018. "Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions," Energies, MDPI, vol. 11(5), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1143-:d:144522
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    References listed on IDEAS

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    1. de Simón-Martín, Miguel & Alonso-Tristán, Cristina & Díez-Mediavilla, Montserrat, 2017. "Diffuse solar irradiance estimation on building's façades: Review, classification and benchmarking of 30 models under all sky conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 783-802.
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    Cited by:

    1. Evgeny Lisin & Galina Kurdiukova & Pavel Okley & Veronika Chernova, 2019. "Efficient Methods of Market Pricing in Power Industry within the Context of System Integration of Renewable Energy Sources," Energies, MDPI, vol. 12(17), pages 1-16, August.
    2. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
    3. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    4. Anna Ostrowska & Tomasz Sikorski & Alessandro Burgio & Michał Jasiński, 2023. "Modern Use of Prosumer Energy Regulation Capabilities for the Provision of Microgrid Flexibility Services," Energies, MDPI, vol. 16(1), pages 1-13, January.
    5. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    6. Yeji Lee & Doosung Choi & Yongho Jung & Myeongjin Ko, 2022. "Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea," Energies, MDPI, vol. 15(22), pages 1-22, November.
    7. Bojan Kranjec & Sasa Sladic & Wojciech Giernacki & Neven Bulic, 2018. "PV System Design and Flight Efficiency Considerations for Fixed-Wing Radio-Controlled Aircraft—A Case Study," Energies, MDPI, vol. 11(10), pages 1-12, October.
    8. Dennis Dreier & Mark Howells, 2019. "OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling," Energies, MDPI, vol. 12(7), pages 1-26, April.

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