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Photovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks

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A Practical Guide for Advanced Methods in Solar Photovoltaic Systems

Part of the book series: Advanced Structured Materials ((STRUCTMAT,volume 128))

Abstract

The main goal of this chapter is to show the set up a well-defined method to identify and properly train the hybrid artificial neural network both in terms of number of neurons, hidden layers and training set size in order to perform the day-ahead power production forecast applicable to any photovoltaic (PV) plant, accurately. Therefore, this chapter has been addressed to describe the adopted hybrid method (PHANN—Physic Hybrid Artificial Neural Network) combining both the deterministic clear sky solar radiation algorithm (CSRM) and the stochastic artificial neural network (ANN) method in order to enhance the day-ahead power forecast. In the previous works, this hybrid method had been tested on different PV plants by assessing the role of different training sets varying in the amount of data and number of trials, which should be included in the “ensemble forecast.” In this chapter, the main results obtained by applying the above-mentioned procedure specifically referred to the available data of the PV power production of a single PV module are presented.

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Authors and Affiliations

  1. Department of Energy, Politecnico di Milano, 20133, Milan, Italy

    E. Ogliari & A. Nespoli

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  1. E. Ogliari
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  2. A. Nespoli
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Correspondence to E. Ogliari .

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Editors and Affiliations

  1. Renewable Energy Laboratory, University of Jijel, Jijel, Algeria

    Adel Mellit

  2. Faculty of Science, Physics Department, Islamic University, Madinah, Saudi Arabia

    Mohamed Benghanem

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Ogliari, E., Nespoli, A. (2020). Photovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks. In: Mellit, A., Benghanem, M. (eds) A Practical Guide for Advanced Methods in Solar Photovoltaic Systems. Advanced Structured Materials, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-43473-1_10

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