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travel and improve air quality caused by road traffic, then it is imperative not only to
forecast the spatial pattern of traffic related to air pollution, but also to model energy
consumption and transport demand. Modeling transport energy demand would enable
calculating pollutants caused by transport.
Energy consumption in the transport sector usually depends on many factors such
as vehicular usage, type of car, income, housing size, vehicular type and many other
socioeconomic parameters. Including all the parameters in sectoral energy modeling
is a difficult task since it requires much detailed study and also considerable data, for
which many of the data are unavailable [8].
Robust estimations of transport energy issues may require a better selection of in-
dependent variables, such as socioeconomic indicators, to determine how much each
parameter contributes to energy consumption. It also requires robust estimation tech-
niques as well as better representations of independent variables. Therefore, estima-
tions of transport energy demand based on the socio-economic and vehicle related
parameters cannot be linear in nature. This is due to the general trend of the economic
and vehicular factors. For example, it is better to represent the Gross Domestic Prod-
uct (GDP) as a non-liner form. Thus, fitting non-linear models for estimating the en-
ergy demand may be better modeled.
The non-linearity of the economic indicators and veh-km to estimate energy de-
mand lead to investigate the different form of solution approaches to a problem such
as harmony search (HS). The available data are partly used for finding the optimal, or
near optimal values of parameters, and for testing of the HS models. A robust model
should be capable of displaying the sensitivity of energy consumption with the related
indicators: population, GDP and veh-km. Meta-heuristic models are usually based on
the assumption that a methodology is chosen and formed according to the minimiza-
tion of some objective function. For this purpose, the HS algorithm is proposed to
model energy consumption in the transport sector.
This chapter is organized as follows:
•
Section 2: A literature survey of energy demand modeling is given.
•
Section 3: The transport energy demand problem formulation is described.
•
Section 4: Illustrative examples are given.
•
Section 5: Conclusions are drawn.
2 Literature Survey of Energy Demand Modeling
For many countries, oil imports are usually the largest share of total imports since the
transport sector is one of the major consumers of energy production in the world. Oil
is the source of about one-fifth of the primary energy in the world [9]. It is also re-
sponsible for almost 60% of the energy consumption in Organization for Economic
Co-operation and Development (OECD) countries and its rate of consumption is in-
creasing in the developing world [10].
One of the main reasons for the increasing demand for energy use may be due to
the rapid increase in population, mobility, businesses, globalization and transport de-
mand. When a high level of energy demand and price levels is considered, oil repre-
sents one of the biggest shares of total energy consumption in many countries [11].
Studies on energy forecasting in Turkey are planned by the Ministry of Energy and
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