Forecasting the transport energy demand based on PLSR method in China

Zhang, Ming; Mu, Hailin; Li, Gang; Ning, Yadong

doi:10.1016/j.energy.2009.06.032

Cited by 135 publications

(59 citation statements)

References 15 publications

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“…Assuming efficiency improvements over time, the energy consumption of rail, road, aviation and water in 2020 will be 20,460, 368,706, 20,432 and 29,983 ktce, respectively. If pipeline and non-specified transport energy consumption are considered, the total transport energy consumption in 2020 will be 443,126 ktce, which is close to the results provided by the ERI [50] and Zhang et al [51]. The annual growth rate of transport energy consumption from 2011 to 2020 is expected to be 4.4%, which is higher than the national total energy consumption (3.8%).…”

Section: Discussionsupporting

confidence: 60%

“…The energy demand in the transport sector was expected to grow by 4.4% from 2015 to 2030, which is equal to the growth rate obtained in this paper using a DEA approach for the period 2011 to 2020. As summarized by Wang et al [49], ERI [50] and Zhang et al [51], they employed a partial least squares regression and presented similar results with two scenarios. The estimation results obtained using DEA are higher than those of the IEA and between the results of the two scenarios produced by the partial least squares regression.…”

Section: Estimation Of Future Energy Consumption Of Transport In Chinamentioning

confidence: 74%

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Estimating Energy Consumption of Transport Modes in China Using DEA

et al. 2015

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Abstract:The rapid growth of transport requirements in China will incur increasing transport energy demands and associated environmental pressures. In this paper, we employ a generalized data envelopment analysis (DEA) to evaluate the relative energy efficiency of rail, road, aviation and water transport from 1971 to 2011 by considering the energy input and passenger-kilometers (PKM) and freight ton-kilometers (TKM) outputs. The results show that the optimal energy efficiencies observed in 2011 are for rail and water transport, with the opposite observed for the energy efficiencies of aviation and road transport. In addition, we extend the DEA model to estimate future transport energy consumption in China. If each transport mode in 2020 is optimized throughout the observed period, the national transport energy consumption in 2020 will reach 497,701 kilotons coal equivalent (ktce), whereas the annual growth rate from 2011 to 2020 will be 5.7%. Assuming that efficiency improvements occur in this period, the estimated national transport energy consumption in 2020 will be 443,126 ktce, whereas the annual growth rate from 2011 to 2020 will be 4.4%, which is still higher than that of the national total energy consumption (3.8%).

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Section: Discussionsupporting

confidence: 60%

Section: Estimation Of Future Energy Consumption Of Transport In Chinamentioning

confidence: 74%

Estimating Energy Consumption of Transport Modes in China Using DEA

et al. 2015

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“…Parekh et al [1] applied MLR model to project the demand for petroleum products of India from 2011 to 2012. Zhang et al [2] forecasted the transportation energy demand in 2010,2015 and 2020 based on two scenarios. Limanond et al [3] developed log-linear regression models to project the transportation energy consumption in Thailand for 2010-2030.…”

Section: Parametric Techniquesmentioning

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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“…There are a variety of approaches already available for the forecast of the annual energy demand of the transportation sector such as the ones described in [9], [10] and [11] but, as it has been already stated earlier, an hourly distribution is needed here. To accommodate for this, agent based modelling (ABM) has been utilized in the case of this work.…”

Section: Introductionmentioning

confidence: 99%

Agent based modelling and energy planning – Utilization of MATSim for transport energy demand modelling

Novosel

Perković

Ban

et al. 2015

Energy

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The transportation sector is one of the major energy consumers in most energy systems and a large portion of the energy demand is linked to road transport and personal vehicles. It accounted for 32.8% of the final energy consumption of Croatia in 2011 making it the second most energy demanding sector. Because of their higher efficiency, a modal switch from conventional internal combustion engines (ICE) to electric vehicles (EVs) has the potential to greatly reduce the overall energy demand of the transport sector. Our previous work has shown that a transition to EVs in a combination with a modal split from air and road to rail transport can reduce the energy consumption in Croatia by 99 PJ, which is approximately 59%, by the year 2050 when compared to the business as usual scenario. The goal of this paper is to model the hourly distribution of the energy consumption of EVs and use the calculated load curves to test their impact on the Croatian energy system. The hourly demand for the transport sector has been calculated using the agent-based modelling tool MATSim on a simplified geographic layout. The impact EVs have on the energy system has been modelled using EnergyPLAN.

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Forecasting the transport energy demand based on PLSR method in China

Cited by 135 publications

References 15 publications

Estimating Energy Consumption of Transport Modes in China Using DEA

Estimating Energy Consumption of Transport Modes in China Using DEA

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

Agent based modelling and energy planning – Utilization of MATSim for transport energy demand modelling

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