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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 971908, 8 pages
Research Article

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

1NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
2Tree-Lab Instituto Tecnológico de Tijuana, Mesa de Otay, 22500 Tijuana, BC, Mexico

Received 22 January 2015; Revised 16 May 2015; Accepted 18 May 2015

Academic Editor: Thomas DeMarse

Copyright © 2015 Mauro Castelli et al. 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.


Energy consumption forecasting (ECF) is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.