title: Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.
abstract: 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.
authors.affiliation: NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal.
authors.forename: Mauro
authors.identifier: 0000-0002-8793-1451
authors.initials: M
authors.lastname: Castelli
authors.affiliation: Tree-Lab Instituto Tecnológico de Tijuana, Mesa de Otay, 22500 Tijuana, BC, Mexico.
authors.forename: Leonardo
authors.identifier: 0000-0003-1812-5736
authors.initials: L
authors.lastname: Trujillo
authors.affiliation: NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Vanneschi
mesh_terms: D003198:Computer Simulation; D005544:Forecasting; D006801:Humans; D007558:Italy; D008962:Models, Theoretical; D040841:Resource Allocation / Q000706:statistics & numerical data / Q000639:trends*; D012189:Retrospective Studies; D012984:Software