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Review on Application of Artificial Intelligence in Photovoltaic Output Prediction

  • Dianling Huang
  • Xiaoguang Wang
  • Boyao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

With the development of photovoltaic, the distributed power grid has begun large-scale interconnection, which has an impact on the stability of the network. Distributed photovoltaic output is intermittent and stochastic. It is affected by climate and environment conditions such as sunlight, season, geography and time. It is difficult to accurately model and analyze the characteristics of distributed photovoltaic output. More and more artificial intelligence methods are applied to the photovoltaic output prediction and produce good results. This paper introduces the importance of photovoltaic prediction in photovoltaic power generation, then briefly gives what is artificial intelligence, and enumerates a large number of applications of artificial intelligence methods in photovoltaic power prediction. Finally, the direction of future research on photovoltaic power generation is proposed.

Keywords

Photovoltaic output Prediction Artificial intelligence Machine learning Deep learning 

References

  1. 1.
    REN21: Global Renewable Energy Status Report 2018. In: Organizing Committee of Guiyang International Forum on Ecological Civilization, Guiyang (2018)Google Scholar
  2. 2.
    Qian, Z., Cai, S.B., Gu, Y.Q.: Review of PV power generation prediction. Mech. Electr. Eng. Mag. 32(5), 651–659 (2015)Google Scholar
  3. 3.
    Baidu Encyclopedia: Artificial Intelligence. https://baike.baidu.com/item/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/9180. Accessed 11 June 2018
  4. 4.
    Gu, X.F.: Historical review and development history of artificial intelligence. Chin. J. Nat. 38(3), 157–166 (2016)Google Scholar
  5. 5.
    Baidu Encyclopedia: Machine learning. https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/217599?Fr=aladdin. Accessed 06 June 2018
  6. 6.
  7. 7.
    Baidu Encyclopedia: Natural Language Processing. https://baike.baidu.com/item/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%E8%A7%A7%89/2803351?Fr=aladdin. Accessed 06 June 2018
  8. 8.
    Cao, J.Q.: Five core technologies of artificial intelligence. https://blog.csdn.net/sergeycao/article/details/75254630. Accessed 17 July 2017
  9. 9.
    Michael, C.: What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?. https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/. Accessed 19 July 2016
  10. 10.
    Code_xzh: An article to understand the difference between AI, machine learning and in-depth learning. https://blog.csdn.net/xiangzhihong8/article/details/69935712. Accessed 08 Oct 2018
  11. 11.
    Wods_wang_219: Artificial Intelligence Learning Notes - Basic Concepts. https://blog.csdn.net/woods_wang_219/article/details/53519149. Accessed 08 Dec 2016
  12. 12.
    Lecun, Y., Bengio, Y., Hiton, G.: Deep learning. Nature 1(7553), 436–444 (2015)CrossRefGoogle Scholar
  13. 13.
    Deng, L., Yu, D.: Deep learning. Signal Process. 7, 3–4 (2014)Google Scholar
  14. 14.
    Zhang, Q.L., Zhao, D., Chi, X.B.: Review for deep learning based on medical imaging diagnosis. Comput. Sci. 44(b11), 1–7 (2017)Google Scholar
  15. 15.
    Gold FE: Deep learning: The history of in-depth learning. https://blog.csdn.net/u012177034/article/details/52252851. Accessed 19 Aug 2016
  16. 16.
    Jing, B., Tan, L.N., Qian, Z., et al.: An overview of research progress of short-term photovoltaic forecasts. Electr. Meas. Instrum. 54(12), 1–6 (2017)Google Scholar
  17. 17.
    Cheng, H., Cao, W.S.: Forecasting research of long-term solar irradiance and output power for photovoltaic generation system. In: 2012 Fourth International Conference on Computational and Information Sciences, pp. 1224–1227 (2012)Google Scholar
  18. 18.
    Lorenz, E., Hurka, J., Heinemann, D., et al.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2(1), 2–10 (2009)CrossRefGoogle Scholar
  19. 19.
    Yu, T.C., Chang, H.T.: The forecast of the electrical energy generated by photovoltaic systems using neural network method. In: 2011 International Conference on Electric Information and Control Engineering, ICEICE, pp. 2758–2761. IEEE (2011)Google Scholar
  20. 20.
    Tao, C., Duan, S., Chen, C.: Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement. In: IEEE International Symposium on Power Electronics for Distributed Generation Systems, pp. 773–777. IEEE (2010)Google Scholar
  21. 21.
    Yuan, X.L., Shi, J.H., Xu, J.Y.: Short-term power forecasting for photovoltaic generation considering weather type index. Proc. CSEE 33(34), 57–64 (2013)Google Scholar
  22. 22.
    Ding, M., Wang, L., Bi, R.: An ANN-based approach for forecasting the power output of photovoltaic system. Proc. Environ. Sci. 11(1), 1308–1315 (2011)CrossRefGoogle Scholar
  23. 23.
    Gao, Y., Zhang, B.L., Mao, J.L., Liu, Y.: Machine learning-based adaptive very-short-term forecast model for photovoltaic power. Power Syst. Technol. 39(2), 307–312 (2015)Google Scholar
  24. 24.
    Wang, Y., Su, S., Yan, Y.T.: Very short-term PV power forecasting model based on Kalman filter algorithm and BP neutral network. Electr. Eng. 1, 42–46 (2014)Google Scholar
  25. 25.
    Wang, C.L.: Research on the short-term prediction of photovoltaic power output. Master, Nanchang University (2015)Google Scholar
  26. 26.
    Yuan, X.L., Shi, J.H., Xu, J.Y.: Short-term power forecast for photovoltaic generation based on BP neutral network. Renew. Energy Resour. 31(7), 11–16 (2013)Google Scholar
  27. 27.
    Wang, K.: Output prediction research of photovoltaic system considering multiple uncertainty factors. Master, School of Electrical and Electronic Engineering (2013)Google Scholar
  28. 28.
    Zhao, S.Q., Wang, M.Y., Hu, Y.Q., Liu, C.L.: Research on the prediction of PV output based on uncertainty theory. Trans. China Electrotech. Soc. 30(16), 213–220 (2015)Google Scholar
  29. 29.
    Hou, W., Xiao, J., Niu, L.Y.: Analysis of power generation capacity of photovoltaic power generation system in electric vehicle charging station. Electr. Eng. 4, 53–58 (2016)Google Scholar
  30. 30.
    Atsushi, Y., Tomonobu, S., Toshihisa, F., et al.: Decision technique of solar radiation prediction applying recurrent neural network for short-term ahead power output of photovoltaic system. Smart Grid Renew. Energy 04(6), 32–38 (2013)CrossRefGoogle Scholar
  31. 31.
    Badia, A., Xavier, L.P.: Artificial neural network based daily local forecasting for global solar radiation. Appl. Energy 130(5), 333–341 (2014)Google Scholar
  32. 32.
    Mellit, A., Pavan, A.M., Lughi, V.: Short-term forecasting of power production in a large-scale photovoltaic plant. Sol. Energy 105, 401–413 (2014)CrossRefGoogle Scholar
  33. 33.
    Cyril, V., Gilles, N., Soteris, K., Nivet, M.-L.: Machine learning methods for solar radiation forecasting - a review. Renew. Energy 105, 569–582 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computer Network Information CenterChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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