Automatic generation of models for energy demand estimation using Grammatical Evolution
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- @Article{COLMENAR:2018:Energy,
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author = "J. M. Colmenar and J. I. Hidalgo and S. Salcedo-Sanz",
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title = "Automatic generation of models for energy demand
estimation using Grammatical Evolution",
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journal = "Energy",
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volume = "164",
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pages = "183--193",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Energy demand
estimation, Macro-economic variables, Grammatical
evolution, Meta-heuristics",
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ISSN = "0360-5442",
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DOI = "doi:10.1016/j.energy.2018.08.199",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360544218317353",
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abstract = "The estimation of total energy demand in a country
from macro-economic variables is an important problem
useful to evaluate the robustness of the country's
economy. Since the first years of this century,
meta-heuristics approaches have been successfully
applied to this problem, for different countries and
problem's parameterizations. Many of these works
optimize prediction models which are based on classical
polynomial or simple exponential relationships, which
may not be the best option for an accurate energy
demand estimation prediction. In this paper the use of
Grammatical Evolution is proposed to generate new
models for total energy demand estimation at country
level. Grammatical Evolution is a class of Genetic
Programming algorithm, which is able to automatically
generate new models from input variables. In this case,
Grammatical Evolution considers macro-economic
variables from which it is able to generate new models
for total energy demand estimation of a country, with a
temporal prediction horizon of one year. The models
generated by the Grammatical Evolution are further
optimized in order to adjust their parameters to the
energy demand estimation. This process is carried out
by means of a Differential Evolution approach, which is
run for every model generated by the Grammatical
Evolution. Thus, the algorithmic proposal consists of a
hybrid method, involving Grammatical Evolution for
model generation and a Differential Evolution
meta-heuristic for the models' parameter tuning. The
performance of the proposed approach has been evaluated
in two different problems of total energy demand
estimation in Spain and France, with excellent results
in terms of prediction accuracy",
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keywords = "genetic algorithms, genetic programming, Energy demand
estimation, Macro-economic variables, Grammatical
evolution, Meta-heuristics",
- }
Genetic Programming entries for
J Manuel Colmenar
Jose Ignacio Hidalgo Perez
Sancho Salcedo-Sanz
Citations