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International Conference on Smart Computing and Communication

SmartCom 2018: Smart Computing and Communication pp 281–290Cite as

Review on Application of Artificial Intelligence in Photovoltaic Output Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Author information

Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China

    Dianling Huang, Xiaoguang Wang & Boyao Zhang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Dianling Huang & Boyao Zhang

Authors
  1. Dianling Huang
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  2. Xiaoguang Wang
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  3. Boyao Zhang
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Corresponding author

Correspondence to Xiaoguang Wang .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Cite this paper

Huang, D., Wang, X., Zhang, B. (2018). Review on Application of Artificial Intelligence in Photovoltaic Output Prediction. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_28

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  • DOI: https://doi.org/10.1007/978-3-030-05755-8_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05754-1

  • Online ISBN: 978-3-030-05755-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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