Forecast of energy consumption of vehicles

Эдер, Л В; Nemov, Vasily

doi:10.1134/s1075700717040049

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…Accordingly, within the traditional approach to automobilization analysis and forecasting, it is usually assumed that car ownership dynamics can be approximated with an acceptable level of adequacy by the class of S-shaped functions (logistic function [1,22,28]; Gompertz function [21,26,27,29]; quasi-logical function [8]; and the power growth curve [2,3]), the asymptotic limit of which characterizes the probable level of filling (saturating) the need for personal cars in the considered country (Fig. 5).…”

Section: Factors Limiting Automobilizationmentioning

confidence: 99%

“…An unmanned vehicle that is integrated into a network and interacts with other cars and infrastructure Vol. 29 No. 4 2018 KSENOFONTOV, MILYAKIN has an even greater potential for effective use.…”

Section: Factors Limiting Automobilizationmentioning

confidence: 99%

“…This system of ideas about automobilization processes is presented and developed in works and others. In Russian researches it is presented by works [28,29] and others.…”

mentioning

confidence: 99%

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The Automobilization Process and Its Determining Factors in the Past, Present, and Future

Ksenofontov

Milyakin

2018

Stud. Russ. Econ. Dev.

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Section: Factors Limiting Automobilizationmentioning

confidence: 99%

Section: Factors Limiting Automobilizationmentioning

confidence: 99%

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The Automobilization Process and Its Determining Factors in the Past, Present, and Future

Ksenofontov

Milyakin

2018

Stud. Russ. Econ. Dev.

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“…the core, aiming to alleviate the pressure of energy and environment [5]. The scientific management of energy refers to the use of scientific principles and methods to systematically investigate and quantitatively analyze all aspects of the energy system, so as to use energy in a planned and organized manner to achieve effective, economical and rational development and utilization of energy [6]. The energy management system is a powerful tool for enterprises to achieve energy science management [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Fan

2019

IEEE Access

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In the context of increasingly tight energy supply and rising prices, it is of great significance to carry out research on energy consumption prediction models with energy conservation as the goal. In order to improve energy efficiency, it is not only necessary to conduct statistics and analysis on energy historical data, but also to predict future energy data. In this paper, Bicubic interpolation algorithm and convolutional neural network are used to spatially predict energy consumption. The model framework structure for energy consumption prediction is given, and the two models are experimentally analyzed. The results show that the convolutional neural network is better than the Bicubic interpolation method, and the prediction result is closer to the actual value. For the high spatial complexity of energy consumption data, the definition and framework of the RessNetGAN model are first given. Secondly, the specific structure of the generator is given. How to extract the spatial eigenvalue of energy consumption through 3D convolution operation is introduced, and the experiment is carried out by changing the pooling parameters of the energy consumption to be predicted. The results show that the RessNetGAN model with generated confrontation structure has better prediction performance than the convolutional neural network model, and when the input energy consumption data is very low precision, there will be no serious distortion of the prediction result. INDEX TERMS Energy consumption forecasting, deep learning, generating confrontation networks, spatial domain reconstruction.

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“…The study of demand for cars using alternative energy sources was carried out by Mabit [9], using such factors as price, annual costs, acceleration time and operating range.…”

Section: Introductionmentioning

confidence: 99%

Identification of factors affecting sustainable development of renewable energy sources in transport

et al. 2019

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At present motor transport is one of the key components of the formation of a long-term policy of sustainable economic development. Analysis of the structure of energy consumption in motor transport is necessary for better understanding of the factors that affect the increase in the share of renewable energy sources in transport and the effectiveness of specific support measures for the transition to vehicles with minimal or no greenhouse gas emissions. Models of pooled regression, regression with random and fixed effects were built in order to reveal the influence of the chosen factors on the share of renewable energy and electricity in the structure of energy consumption by vehicles. The results of evaluation for 31 European countries in the 2005-2017 showed that the criterion, which allows identifying groups of countries, is the volume of environmental transport taxes per vehicle unit. Countries with higher taxation volumes has such influencing factors as Corruption Perceptions Index, Energy productivity, Total environmental taxes per capita and Employment in high- and medium-high technology manufacturing sectors and knowledge-intensive service sectors. Consumption in countries with lower tax differentiation is influenced by index of research intensity, Corruption Perceptions Index and Shares of environmental and labour taxes.

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Forecast of energy consumption of vehicles

Cited by 15 publications

References 2 publications

The Automobilization Process and Its Determining Factors in the Past, Present, and Future

The Automobilization Process and Its Determining Factors in the Past, Present, and Future

Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Identification of factors affecting sustainable development of renewable energy sources in transport

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