Transport energy demand forecast using multi-level genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Forouzanfar2012496,
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author = "Mehdi Forouzanfar and A. Doustmohammadi and
Samira Hasanzadeh and H. {Shakouri G}",
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title = "Transport energy demand forecast using multi-level
genetic programming",
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journal = "Applied Energy",
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volume = "91",
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number = "1",
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pages = "496--503",
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year = "2012",
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ISSN = "0306-2619",
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DOI = "doi:10.1016/j.apenergy.2011.08.018",
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URL = "http://www.sciencedirect.com/science/article/pii/S0306261911005149",
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keywords = "genetic algorithms, genetic programming, Transport
energy demand, Forecasting, Modelling",
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abstract = "In this paper, a new multi-level genetic programming
(MLGP) approach is introduced for forecasting transport
energy demand (TED) in Iran. It is shown that the
result obtained here has smaller error compared with
the result obtained using neural network or fuzzy
linear regression approach. The forecast uses
historical energy data from 1968 to 2002 and it is
based on three parameters; gross domestic product
(GDP), population (POP), and the number of vehicles
(VEH). The approach taken in this paper is based on
genetic programming (GP) and the multi-level part of
the name comes from the fact that we use GP in two
different levels. At the first level, GP is used to
obtain the time series model of the three parameters,
GDP, POP, and VEH, and forecast those parameters for
the time interval that their actual data are not
available, and at the second level GP is used one more
time to forecast TED based on available data for TED
along with the data that are either available or
predicted for the three parameters discussed earlier.
Actual data from 1968 to 2002 are used for training and
the data for years 2003-2005 are used to test the GP
model. We have limited ourselves to these data ranges
so that we could compare our results with the existing
ones in the literature. The estimation GP for the model
is formulated as a nonlinear optimisation problem and
it is solved numerically.",
- }
Genetic Programming entries for
Mehdi Forouzanfar
Ali Doustmohammadi
Samira Hasanzadeh
G Hamed Shakouri
Citations