Residential-commercial energy input estimation based on genetic algorithm (GA) approaches: an application of Turkey

Abstract The main objective of the present study is to develop the energy input estimation equations for the residential-commercial sector (RCS) in order to estimate the future projections based on genetic algorithm (GA) notion and to examine the effect of the design parameters on the energy input of the sector. For this purpose, the Turkish RCS is given as an example. The GA Energy Input Estimation Model (GAEIEM) is used to estimate Turkey’s future residential-commercial energy input demand based on GDP, population, import, export, house production, cement production and basic house appliances consumption figures. It may be concluded that the three various forms of models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

[1]  Arif Hepbasli,et al.  Development of energy efficiency and management implementation in the Turkish industrial sector , 2003 .

[2]  Christos A. Frangopoulos,et al.  Operation optimization of an industrial cogeneration system by a genetic algorithm , 1997 .

[3]  Dorota Chwieduk,et al.  Towards sustainable-energy buildings , 2003 .

[4]  Ibrahim Dincer,et al.  Energy intensities for Canada , 1996 .

[5]  OLCAY ERSEL CANYURT,et al.  Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches , 2004 .

[6]  Wang Xiaohua,et al.  Survey of rural household energy consumption in China , 1996 .

[7]  Ibrahim Dincer,et al.  Energy and exergy use in public and private sector of Saudi Arabia , 2004 .

[8]  Sedat Keleş,et al.  Energy utilization, environmental pollution and renewable energy sources in Turkey , 2004 .

[9]  Sukran Dilmac,et al.  A comparision of new Turkish thermal insulation standard (TS 825), ISO 9164, EN 832 and German regulation , 2003 .

[10]  David Coley,et al.  Low-energy design: combining computer-based optimisation and human judgement , 2002 .

[11]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[12]  Celso Marcelo Franklin Lapa,et al.  A niching genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization , 2003 .

[13]  Arif Hepbasli,et al.  Comparison of Turkey's Sectoral Energy Utilization Efficiencies between 1990 and 2000, Part 2: Residential-Commercial and Transportation Sectors , 2004 .

[14]  Eldon J. Gardner Principles of Genetics , 1961 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  Mahmoud A. Abo-Sinna,et al.  A solution to the optimal power flow using genetic algorithm , 2004, Appl. Math. Comput..

[17]  Soteris A. Kalogirou,et al.  Optimization of solar systems using artificial neural-networks and genetic algorithms , 2004 .

[18]  Arif Hepbasli,et al.  Simple Correlations for Estimating the Energy Production of Turkey , 2002 .

[19]  Arif Hepbasli,et al.  A study on the evaluation of energy utilization efficiency in the Turkish residential-commercial sector using energy and exergy analyses , 2003 .

[20]  Nan Zhang,et al.  A genetic-algorithm-based experimental technique for determining heat transfer coefficient of exterior wall surface , 2004 .