| Issue |
Renew. Energy Environ. Sustain.
Volume 1, 2016
|
|
|---|---|---|
| Article Number | 39 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/rees/2016047 | |
| Published online | 30 August 2016 | |
Research Article
Solar and wind forecasting by NARX neural networks
National Research Council of Italy, Institute of Intelligent Systems for Automation, Unit of Palermo,
Palermo, Italy
⁎ e-mail: gianpaolo.vitale@ieee.org
The nonlinear autoregressive network with exogenous input (NARX) is used to perform hourly solar irradiation and wind speed forecasting, according to a multi-step ahead approach. Temperature has been considered as the exogenous variable. The NARX topology selection is supported by a combined use of two techniques: (1) a genetic algorithm (GA)-based optimization technique and (2) a method that determines the optimal network architecture by pruning (optimal brain surgeon (OBS) strategy). The considered variables are observed at hourly scale in a seven year dataset and the forecasting is done for several time horizons in the range from 8 to 24 h ahead.
© A. Di Piazza et al., published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.