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Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Author

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  • Dong, Jin
  • Olama, Mohammed M.
  • Kuruganti, Teja
  • Melin, Alexander M.
  • Djouadi, Seddik M.
  • Zhang, Yichen
  • Xue, Yaosuo

Abstract

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.

Suggested Citation

  • Dong, Jin & Olama, Mohammed M. & Kuruganti, Teja & Melin, Alexander M. & Djouadi, Seddik M. & Zhang, Yichen & Xue, Yaosuo, 2020. "Novel stochastic methods to predict short-term solar radiation and photovoltaic power," Renewable Energy, Elsevier, vol. 145(C), pages 333-346.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:333-346
    DOI: 10.1016/j.renene.2019.05.073
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    Citations

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    Cited by:

    1. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.
    4. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
    5. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
    6. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
    7. Park, Byungkwon & Dong, Jin & Liu, Boming & Kuruganti, Teja, 2023. "Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks," Applied Energy, Elsevier, vol. 330(PA).
    8. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    9. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    10. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    11. Ming Meng & Chenge Song, 2020. "Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    12. Stephan Schlüter & Fabian Menz & Milena Kojić & Petar Mitić & Aida Hanić, 2022. "A Novel Approach to Generate Hourly Photovoltaic Power Scenarios," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    13. Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    14. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
    15. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).

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