Academic journal article Iranian Journal of Management Studies

Selecting the Appropriate Scenario for Forecasting Energy Demands of Residential and Commercial Sectors in Iran Using Two Metaheuristic Algorithms

Academic journal article Iranian Journal of Management Studies

Selecting the Appropriate Scenario for Forecasting Energy Demands of Residential and Commercial Sectors in Iran Using Two Metaheuristic Algorithms

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

Today, demand management, including the demand for energy, plays a significant role in a country's planning process. However, it has a vital role to play in satisfying economic security so that as an important factor, it plays a direct/indirect role in the production processes of all economic sectors. On the other hand, some factors, such as the implementation of development projects, trend of industrialization in Iran, and the population growth have highlighted the demand for energy carriers (Assareh, Behrang, Assari, and Ghanbarzadeh, 2010). The abundance of oil reserves in Iran and the polices associated with supplying cheap energy carriers have converted different economic sectors to energy consumer units in recent years, so that the domestic production of energy carriers, namely, domestically produced fuel, was not sufficient for purposes of internal consumption, and a fraction of the foreign exchange is allocated to supply the required fuel (Karbassi, Abduli, and Mahin Abdollahzadeh, 2007). According to the latest data obtained from the portal of the Iranian Ministry of Energy (MOE) in 2011, the energy consumption of residential and commercial sectors was 58.8 mega tons of crude oil equivalent (Mtoe)11which is a considerable value compared with other sectors. Therefore, predicting the energy demand of these sectors is an important exercise to be undertaken for a proper planning of demand management ("Ministry of Energy. Energy balance annual report. Tehran, Iran," 2012). Energy consumption modeling and predicting are comprehensive subjects which have attracted the attention of many scientists and engineers, and have highlighted both energy production and energy consumption subjects (Ozturk and Ceylan, 2005). Energy planning cannot be practiced without having an acceptable level of knowledge about energy consumption in the past and present, and in predicting the probable energy demand in the future (Sözen, Gülseven, and Arcaklioglu, 2007). The demand for energy is estimated based on economic and noneconomic indices obtained, probably via linear and nonlinear statistical and mathematical methods as well as simulated models. The nonlinear indices on the one hand and the demand for energy on the other hand have triggered the process of seeking intelligent solutions such as genetic algorithm, the particle swarm optimization (PSO) algorithm, fuzzy-based regression and neural networks (Azadeh and Tarverdian, 2007). The objectives of the study are: (1) selecting the appropriate scenario to predict the energy demands of residential and commercial sectors in Iran; (2) prediction of the annual energy demand of residential and commercial sectors in Iran up to 2032.

Literature Review

There is no doubt as to the importance of the role that energy plays in the lives of people today, and how significant a role it has been even in international relations among nations. The industrial revolution and much of its fast growth, the development of technologies, and the ever increasing facilities which provide welfare for residents are all in some way dependent on energy. The importance of energy has been even more emphasized especially after the energy crisis, and consequently, its impact on the world economy was investigated by many researchers. Residential and commercial sectors consume the biggest amounts of energy in Iran, achieving about 34% of the total energy consumption in 2012. Thus, forecasting residential and commercial demand for energy becomes an essential function in planning for the future, in order to design more efficient systems and control the demand by a proper price mechanism as well (Shakouri and Kazemi, 2011). Many studies have been carried out on intelligent algorithms in order to predict the demand for energy. In the following section, a number of the studies, empirically and theoretically, are presented in Tables 1 and 2.

The aims of econometric methods consist of experimental investigation into the economic relationships in economic theories, forecasting and decision making, and also evaluating the forecast made by any new policy. …

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