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Photovoltaic Power Prediction Model Based on Parallel Neural Network and Genetic Algorithms

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Management of Information, Process and Cooperation (MIPaC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 686))

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Abstract

With the wide application of large-scale photovoltaic systems, photovoltaic power prediction can reduce the negative effects caused by the intermittency and randomness of output power for photovoltaic system. This paper proposes a novel photovoltaic power prediction model based on parallel back propagation neural network (BPNN) and genetic algorithms to predict output power, whose input parameters are historical power output data, historical meteorology data, and meteorology data of the objective day. A parallel BPNN algorithm based on MapReduce is proposed to establish a mapping relationship between input and output through studying large amounts of training sample data. Furthermore, a parallel genetic algorithm based on MapReduce is proposed to optimize BPNN initial weights and thresholds. Experiment results show that the proposed model with parallel BPNN and genetic algorithms can significantly improve prediction accuracy and speed, compared with traditional photovoltaic power prediction model.

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Acknowledgement

The research work presented in this paper is partially supported by the Scientific Research Projects of the NSFC (Grant No. 61173015, 61573257) and Hangzhou Municipal Science and Technology Bureau of social development and scientific research projects (No. 20150533B16).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Tongji University, No. 4800, Caoan Road, Shanghai, China

    Gaowei Xu & Min Liu

Authors
  1. Gaowei Xu
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  2. Min Liu
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Correspondence to Min Liu .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Holy See (Vatican City State)

    Jianxun Liu

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Xu, G., Liu, M. (2017). Photovoltaic Power Prediction Model Based on Parallel Neural Network and Genetic Algorithms. In: Cao, J., Liu, J. (eds) Management of Information, Process and Cooperation. MIPaC 2016. Communications in Computer and Information Science, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-10-3996-6_8

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  • DOI: https://doi.org/10.1007/978-981-10-3996-6_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3995-9

  • Online ISBN: 978-981-10-3996-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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