Genetic algorithm approach to estimate transport energy demand in Turkey

Haldenbilen, Soner; Ceylan, Halim

doi:10.1016/s0301-4215(03)00202-7

Cited by 116 publications

(53 citation statements)

References 5 publications

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“…Today, developed countries use 70% of global fossil energy annually and developing countries, with 75% of the world population, consume only 30% of the world's fossil energy (Pimentel & Pimentel, 2008). Between 1945 and 1985, global total energy consumption increased 500% and the petroleum and natural gas consumption increased by about 900%, while the world's population increased by 200% (Haldenbilen & Ceylan, 2005). Studies show that some environmental impacts, such as sulphur dioxide, surface ozone, smog levels, and especially, O 3 concentration, may significantly reduce the yield of several agricultural crops, such as wheat, soybean, and corn (Aunan et al, 2000).…”

Section: Interaction Effects Between Energy Environment and Agriculturementioning

confidence: 99%

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

115

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New Zealand farmers practice a form of ‗industrialised' agriculture that relies on relatively high inputs of fossil fuels, not only to power machinery directly but also for manufacturing artificial fertilizers and agrichemicals (Wells, 2001). Consequently, New Zealand is one of the countries with the highest energy input per unit weight (in agriculture) in the world (Conforti & Giampietro, 1997). Furthermore, in terms of shipping, the influence of increasing global fuel costs is greater on New Zealand farming than in other countries. The main aim of this study was to estimate energy consumption in wheat production. Energy determination can give a clear picture of farms in order to compare different farming systems and energy inputs. The second main target of this study was to develop a neural network model to simulate and predict energy use in wheat production under different conditions incorporating social, geographical, and technical factors. Additionally, the interaction effects between different factors were examined in this study.This study was conducted on irrigated and dryland wheat fields in Canterbury, New Zealand, in the 2007-2008 harvest year. Canterbury represents 87% of the wheat area and 66% of the arable area harvested in New Zealand.Energy consumption here is defined as the energy used for the production of wheat until it leaves the farm. The data were collected from three different sources:questionnaire, literature review, and field measurements. The energy inputs estimated in this study are those that go into on-farm production systems before the post-harvest processes. The study considered only the energy used in wheat production, without taking into account the natural sources of energy (radiation, wind, rain, etc). IVA survey was conducted to collect the most important data and to identify farmers' attitudes and opinions about energy consumption. In this study, 40 arable farms were selected randomly, as far as possible. From the initial analysis, it was found that 30 farms were irrigated and the rest were dryland farms. Irrigated farms were irrigated between one to ten times annually depending on the rainfall. Some irrigated farms have also been converted to dryland farms, or vice versa, in different years. The data for a large number of farming factors were gathered in the survey.Average energy consumption for wheat production was estimated at around 22,600 MJ/ha. On average, fertilizer and electricity (mostly for irrigation) were used more than other energy sources, at around 10,654 MJ/ha (47%) and 4,870 MJ/ha (22%), respectively. The average energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. This study is the first to create an appropriate Artificial Neural Network (ANN) model to predict energy consumption in wheat production with optimum variables. This study would be the first to investigate the factors related to the efficient use of energy in agricultural production. A careful study o...

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Section: Interaction Effects Between Energy Environment and Agriculturementioning

confidence: 99%

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

115

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“…GA is used for energy demand model in linear, quadratic or exponential forms. Researchers such as Ceylon and Ozturk [9] , Haldenbilen and Ceylon [10] studied total energy demand of Iran and transport energy demand in the same way. Assarch et al [11] applied GA techniques to estimate the oil demand in Iran, based on socioeconomic indicators in which the models are developed in exponential and linear forms.…”

Section: 23mentioning

confidence: 99%

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Anand¹,

Suganthi²

2017

Preprint

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In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , per capita demand, income and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between GSDP and the electricity demand of the state.

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“…The GAs forecasting approach has been applied to a wide range of disciplines in recent times, including electric energy estimation (Ozturk et al 2005), energy demand prediction (Ghanbari et al 2013), housing price forecasting (Jirong et al 2011), tourism demand forecasting (Hernández-López and Cáceres-Hernández 2007; Hong et al 2011), traffic accident severity prediction (Akgüngör and Doğan 2009;Kunt et al 2011), and transport energy demand prediction (Haldenbilen and Ceylan 2005). Despite the reported benefits of GAs, there has only been one reported study that has applied GAs in the aviation industry.…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

“…Fitness evaluation involves defining an objective or fitness function against which each chromosome is tested for suitability for the environment that is being considered in the study (Hurley et al 1998). The GA selects the fittest members of the population based upon the best fitness value (Haldenbilen and Ceylan 2005). The present study's GA process is illustrated in Fig.…”

Section: Genetic Algorithm Processmentioning

confidence: 99%

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Srisaeng

Baxter

Richardson³

et al. 2015

J.Aerosp. Technol. Manag.

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This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia's domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively.

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Genetic algorithm approach to estimate transport energy demand in Turkey

Cited by 116 publications

References 5 publications

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

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