Next Article in Journal
1H-NMR Spectroscopy: A Possible Approach to Advanced Bitumen Characterization for Industrial and Paving Applications
Next Article in Special Issue
Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm
Previous Article in Journal
High-Frame-Rate Doppler Ultrasound Using a Repeated Transmit Sequence
Previous Article in Special Issue
Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
Article

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Dipartimento di Energia, Politecnico di Milano, via La Masa 34, 20156 Milano, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 31 December 2017 / Revised: 24 January 2018 / Accepted: 28 January 2018 / Published: 2 February 2018
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network’s main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility. View Full-Text
Keywords: photovoltaics; power forecasting; artificial neural networks photovoltaics; power forecasting; artificial neural networks
Show Figures

Graphical abstract

MDPI and ACS Style

Dolara, A.; Grimaccia, F.; Leva, S.; Mussetta, M.; Ogliari, E. Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning. Appl. Sci. 2018, 8, 228. https://0-doi-org.brum.beds.ac.uk/10.3390/app8020228

AMA Style

Dolara A, Grimaccia F, Leva S, Mussetta M, Ogliari E. Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning. Applied Sciences. 2018; 8(2):228. https://0-doi-org.brum.beds.ac.uk/10.3390/app8020228

Chicago/Turabian Style

Dolara, Alberto, Francesco Grimaccia, Sonia Leva, Marco Mussetta, and Emanuele Ogliari. 2018. "Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning" Applied Sciences 8, no. 2: 228. https://0-doi-org.brum.beds.ac.uk/10.3390/app8020228

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop