Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer

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@Article{Castelli:2015:CINS,
  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Leonardo Trujillo",
  title =        "Energy Consumption Forecasting using Semantics Based
                 Genetic Programming with Local Search Optimizer",
  journal =      "Computational Intelligence and Neuroscience",
  year =         "2015",
  volume =       "2015",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464001/",
  URL =          "http://www.ncbi.nlm.nih.gov/pubmed/26106410",
  URL =          "http://downloads.hindawi.com/journals/cin/2015/971908.pdf",
  DOI =          "doi:10.1155/2015/971908",
  size =         "9 pages",
  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.",
  notes =        "Article ID 971908",
}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Leonardo Trujillo

Citations