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A Practical Guide for Advanced Methods in Solar Photovoltaic SystemsPhotovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks

A Practical Guide for Advanced Methods in Solar Photovoltaic Systems: Photovoltaic Plant Output... [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide for Advanced Methods in Solar Photovoltaic SystemsPhotovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks

Part of the Advanced Structured Materials Book Series (volume 128)
Editors: Mellit, Adel; Benghanem, Mohamed

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References (32)

Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
ISBN
978-3-030-43472-4
Pages
203 –222
DOI
10.1007/978-3-030-43473-1_10
Publisher site
See Chapter on Publisher Site

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.]

Published: May 28, 2020

Keywords: Artificial Neural Networks; Day-ahead forecast; Computational Intelligence; PHANN

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