Estimating Transport Energy Demand Using Ant Colony Optimization

Başkan, Özgür; Haldenbilen, Soner; Ceylan, Hüseyin

doi:10.1080/15567240903030513

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Indirect communication among ants via pheromone trails enables them to find shortest paths between their nest and food sources. However, basic ACO requires the problem to be presented as a discrete one . Thus, an ACO algorithm designed for continuous optimizing problems is applied .…”

Section: Methodsmentioning

confidence: 99%

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Zhang

Liu

2016

Journal of Chemometrics

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The accurate and reliable real‐time prediction of melt index (MI) is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a real‐time soft sensor based on optimized least squares support vector machine (LSSVM) for MI prediction. First, the hybrid continuous ant colony differential evolution algorithm (HACDE) is proposed to optimize the parameters of LSSVM. Then, considering the complexity and nondeterminacy of PP plant, an online correcting strategy (OCS) is adopted to update the modeling data and to revise the model's parameters adaptively. Thus, the real‐time prediction model, HACDE‐OCS‐LSSVM, is obtained. Based on the data from a real PP plant, the models of HACDE‐LSSVM, DE‐LSSVM and LSSVM are also developed for comparison. The research results show that the proposed real‐time model achieves a good performance in the practical industrial MI prediction process. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Zhang

Liu

2016

Journal of Chemometrics

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“…Seven ANN models were developed, where the variable sets differentiate considerably, and previous models in the literature with different variables are compared. Natural Resources projection for 2025 [21]. The population, number of vehicle registrations, total annual vehicle-km, gross product, oil prices, import, export, gross domestic and national product, urbanization rate, passenger turnover and freight turnover, as well as socio-economic and transport related indicators data were used in the modeling studies of TED [22,23].…”

Section: Literature Reviewmentioning

confidence: 99%

An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

Çodur

Ünal²

2019

PROMET

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The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.

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“…The proposed model was applied to indicate Turkey's net electricity energy generation and demand until 2025. Baskan et al [146] used ACO for estimating the transport energy demand of Turkey using gross domestic product, population, and vehicle-km. It is also expected that the work will be helpful in developing highly applicable and productive planning for transport energy policies.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Bio-Inspired Optimization of Sustainable Energy Systems: A Review

Zheng

Chen

Yao

et al. 2013

Mathematical Problems in Engineering

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Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

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Estimating Transport Energy Demand Using Ant Colony Optimization

Cited by 9 publications

References 19 publications

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

Bio-Inspired Optimization of Sustainable Energy Systems: A Review

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