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Photovoltaic Power Prediction Using Recurrent Neural Networks

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Book cover Modeling, Identification and Control Methods in Renewable Energy Systems

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

The intermittent characteristic of the photovoltaic power, due to the variability of the weather conditions, involves many problems in grid energy management. Therefore, the PV power forecasting becomes crucial to ensure grid stability and economic dispatch. Artificial neural network (ANN) techniques present alternative approaches to solve nonlinear problems in various areas. They can be trained and applied for prediction. A particular type of ANN namely the recurrent neural network (RNN) has shown powerful capabilities for PV power forecasting. The paper investigates and compares the efficiency of several RNN architectures specifically the modified Elman, Jordan and the hybrid model combining the latest topologies.

The useful data for prediction are acquired from the National Institute of Meteorology. The performance of these topologies is validated by calculating the Root Mean Squared Error, the Mean Absolute Error and the Correlation Factor. The results show that forecasting with the modified Elman outperforms the Jordan and the hybrid networks.

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Authors and Affiliations

  1. Sfax Engineering School, University of Sfax, Sfax, Tunisia

    Rim Ben Ammar & Abdelmajid Oualha

Authors
  1. Rim Ben Ammar
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  2. Abdelmajid Oualha
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Editor information

Editors and Affiliations

  1. Sfax Engineering School, University of Sfax, Sfax, Tunisia

    Nabil Derbel

  2. University of the West of England, Bristol, UK

    Quanmin Zhu

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Ammar, R.B., Oualha, A. (2019). Photovoltaic Power Prediction Using Recurrent Neural Networks. In: Derbel, N., Zhu, Q. (eds) Modeling, Identification and Control Methods in Renewable Energy Systems. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-1945-7_2

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  • DOI: https://doi.org/10.1007/978-981-13-1945-7_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1944-0

  • Online ISBN: 978-981-13-1945-7

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