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Complexity
Volume 2018, Article ID 9327536, 10 pages
https://doi.org/10.1155/2018/9327536
Research Article

Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Department of Computer and Systems Engineering, Universidad de La Laguna, La Laguna 38200 Tenerife, Spain

Correspondence should be addressed to R. M. Aguilar; se.ude.llu@raliugar

Received 30 November 2017; Accepted 25 February 2018; Published 3 April 2018

Academic Editor: José Manuel Andújar

Copyright © 2018 J. M. Torres and R. M. Aguilar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.