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Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production

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  • Ferlito, S.
  • Adinolfi, G.
  • Graditi, G.

Abstract

Actual technology improvements are contributing to DC and AC micro grid diffusion characterized by renewables photovoltaic and storage systems. Photovoltaic technologies have the advantage of a capillary distribution, but they are characterized by an intrinsic variable behavior due to continuously changing weather conditions. This drawback can be overcome by an appropriate temporal and energetic match among photovoltaic generation and storage capacity, so increasing micro grids reliability and efficiency levels. An accurate forecast of photovoltaic production can contribute to smooth photovoltaic systems intermittency problems so supporting generation and storage balance.

Suggested Citation

  • Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:116-129
    DOI: 10.1016/j.apenergy.2017.07.124
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    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances," Renewable Energy, Elsevier, vol. 80(C), pages 770-782.
    3. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
    4. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    5. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    6. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    7. Do, Minh-Thang & Soubdhan, Ted & Benoît Robyns,, 2016. "A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones," Renewable Energy, Elsevier, vol. 85(C), pages 959-964.
    8. Christoph Bergmeir & Rob J Hyndman & Bonsoo Koo, 2015. "A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction," Monash Econometrics and Business Statistics Working Papers 10/15, Monash University, Department of Econometrics and Business Statistics.
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