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Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network

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  • Lan, Hai
  • Zhang, Chi
  • Hong, Ying-Yi
  • He, Yin
  • Wen, Shuli

Abstract

Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect, renewable energy generation has become important in a modern smart grid. However, the characteristics of renewable power generation are volatile and uncertain. This work proposes a new day-ahead spatiotemporal forecasting method for solar irradiation to ensure the efficient operation of power systems. First, the frequency features of a solar irradiation time-series are extracted by discrete Fourier transform (DFT). Principal component analysis (PCA) is then used to identify the crucial frequency features, which are input to an Elman-based neural network to carry out subsequent 24-hour (day ahead) solar irradiation forecasting. Historical weather data from five weather stations that are near the target location are used. Comparative studies of traditional Autoregressive Integrated Moving Average Model (ARIMA), PCA-Back-Propagation (BP)-based neural network, the persistence method, DFT-PCA-BP and the proposed DFT-PCA-Elman method reveal that the proposed method is the most accurate in day-ahead forecasting using spatiotemporal data.

Suggested Citation

  • Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:389-402
    DOI: 10.1016/j.apenergy.2019.04.056
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    1. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    2. Skittides, Christina & Früh, Wolf-Gerrit, 2014. "Wind forecasting using Principal Component Analysis," Renewable Energy, Elsevier, vol. 69(C), pages 365-374.
    3. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    4. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    5. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    6. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2015. "Optimal design of an autonomous solar–wind-pumped storage power supply system," Applied Energy, Elsevier, vol. 160(C), pages 728-736.
    7. André, Maïna & Dabo-Niang, Sophie & Soubdhan, Ted & Ould-Baba, Hanany, 2016. "Predictive spatio-temporal model for spatially sparse global solar radiation data," Energy, Elsevier, vol. 111(C), pages 599-608.
    8. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    9. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    10. C. A. Glasbey & D. J. Allcroft, 2008. "A spatiotemporal auto‐regressive moving average model for solar radiation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 343-355, June.
    11. Kaplani, E. & Kaplanis, S. & Mondal, S., 2018. "A spatiotemporal universal model for the prediction of the global solar radiation based on Fourier series and the site altitude," Renewable Energy, Elsevier, vol. 126(C), pages 933-942.
    12. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    13. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    14. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    15. Dambreville, Romain & Blanc, Philippe & Chanussot, Jocelyn & Boldo, Didier, 2014. "Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model," Renewable Energy, Elsevier, vol. 72(C), pages 291-300.
    16. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    17. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    18. Shireen, Tahasin & Shao, Chenhui & Wang, Hui & Li, Jingjing & Zhang, Xi & Li, Mingyang, 2018. "Iterative multi-task learning for time-series modeling of solar panel PV outputs," Applied Energy, Elsevier, vol. 212(C), pages 654-662.
    19. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    20. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
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