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Heuristic optimization applied to ANN training for predicting renewable energy sources production

Gianmarco Lorenti (Dipartimento di Energia “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, Italy)
Ivan Mariuzzo (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa School of Engineering, Pisa, Italy)
Francesco Moraglio (Gruppo EGEA SpA, Alba, Italy)
Maurizio Repetto (Dipartimento di Energia “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, Italy)

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN: 0332-1649

Article publication date: 15 March 2022

Issue publication date: 3 October 2022

101

Abstract

Purpose

This paper aims to compare stochastic gradient method used for neural network training with global optimizer without use of gradient information, in particular differential evolution.

Design/methodology/approach

This contribute shows the application of heuristic optimization algorithms to the training phase of artificial neural network whose aim is to predict renewable power production as function of environmental variables such as solar irradiance and temperature. The training problem is cast as the minimization of a cost function whose degrees of freedom are the parameters of the neural network. A differential evolution algorithm is substituted to the more usual gradient-based minimization procedure, and the comparison of their performances is presented.

Findings

The two procedures based on stochastic gradient and differential evolution reach the same results being the gradient based moderately quicker in convergence but with a lower value of reliability, as a significant number of runs do not reach convergence.

Research limitations/implications

The approach has been applied to two forecasting problems and, even if results are encouraging, the need for extend the approach to other problems is needed.

Practical implications

The new approach could open the training of neural network to more stable and general methods, exploiting the potentialities of parallel computing.

Originality/value

To the best of the authors’ knowledge, the research presented is fully original for the part regarding the neural network training with differential evolution.

Keywords

Citation

Lorenti, G., Mariuzzo, I., Moraglio, F. and Repetto, M. (2022), "Heuristic optimization applied to ANN training for predicting renewable energy sources production", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 41 No. 6, pp. 2010-2021. https://doi.org/10.1108/COMPEL-11-2021-0420

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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