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Short-term forecasting of solar irradiance

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  • Paulescu, Marius
  • Paulescu, Eugenia

Abstract

Five statistical models for nowcasting solar irradiance are evaluated from different perspectives. The first four models are purely statistical ones: random walk, moving average, exponential smoothing and autoregressive integrated moving average. These models can be considered as benchmarks of different levels of complexity. The fifth model is a version of the two-state model, an applications suite for nowcasting solar irradiance developed by our team. The two-state model connects in an innovative manner an empirical estimator for clear-sky solar irradiance with a statistical predictor for the sunshine number, a binary indicator stating whether the sun shines or not. On the basis of different error metrics, the models’ performances are analyzed from four perspectives: forecast accuracy, forecast precision, data series granularity and variability in data series. The study is conducted with high-quality radiometric data measured at a high frequency of four samples per minute on the Solar Platform of the West University of Timisoara, Romania. No model is ranked as the best, but the peculiarities that cause a model to perform better than others are discussed. By processing information about the atmospheric transmittance, the two-state model proves a slight advance in the forecast accuracy and a notable performance in the forecast precision.

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  • Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:985-994
    DOI: 10.1016/j.renene.2019.05.075
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    2. Puah, Boon Keat & Chong, Lee Wai & Wong, Yee Wan & Begam, K.M. & Khan, Nafizah & Juman, Mohammed Ayoub & Rajkumar, Rajprasad Kumar, 2021. "A regression unsupervised incremental learning algorithm for solar irradiance prediction," Renewable Energy, Elsevier, vol. 164(C), pages 908-925.
    3. Paulescu, Marius & Blaga, Robert & Dughir, Ciprian & Stefu, Nicoleta & Sabadus, Andreea & Calinoiu, Delia & Badescu, Viorel, 2023. "Intra-hour PV power forecasting based on sky imagery," Energy, Elsevier, vol. 279(C).
    4. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    5. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    6. Hartmann, Bálint, 2020. "Comparing various solar irradiance categorization methods – A critique on robustness," Renewable Energy, Elsevier, vol. 154(C), pages 661-671.
    7. Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania," Energies, MDPI, vol. 16(11), pages 1-11, May.
    8. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    9. 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).
    10. Paulescu, Eugenia & Paulescu, Marius, 2021. "A new clear sky solar irradiance model," Renewable Energy, Elsevier, vol. 179(C), pages 2094-2103.
    11. Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "An Ensemble Approach for Intra-Hour Forecasting of Solar Resource," Energies, MDPI, vol. 16(18), pages 1-12, September.
    12. 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.

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