Construction of electricity consumption mathematical models on railway transport used artificial neural network and fuzzy neural network

Komyakov, A. A.; Nikiforov, Mikhail Mikhaylovich; Erbes, V. V.; Cheremisin, Vasily; Ivanchenko, V. I.

doi:10.1109/eeeic.2016.7555450

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…The calculation of the locomotive mechanical power (P) is determined through the product of the speed (v) and the total effort of the locomotives at each point of the specific trajectory, as shown in (6), where u ∑ k=1 F tk (t) can be calculated from (1) as shown in (5), where F kt (t) for a given speed is limited by the forces defined in the tractive and braking efforts profiles of each locomotive. It is worth noting that the total effort of the locomotives may take positive or negative values; hence, P(t) can also be negative or positive if the locomotive is generating or consuming energy, respectively [29].…”

Section: Calculation Of Mechanical Power and Electric Energy Consumptionmentioning

confidence: 99%

“…Regarding the first group, it is worth mentioning that few countries have a reliable database based on measurements of the condition of their railways and locomotives, which can be used to design a road map for the upgrading of their railway infrastructure [5]. This represents a problem when calculating the energy consumption of a railway system since, for historical-data-based methodologies, such as regressions using neural networks or fuzzy logic [6], it would not be possible to have a reliable database to feed the mathematical models. A particular case of historical-data-based methodologies is China, which estimates its energy consumption on trajectories based on data collected from different railway companies [7], facilitating statistical regressions with minimal errors.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for the Estimation of Electrical Power Consumed by Locomotives on Undocumented Railroad Tracks

et al. 2022

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The energy consumption estimation of a locomotive for a particular route is important for the selection of a locomotive technology, the improvement of the energy management system, the evaluation of the locomotive’s potential energy generation, among others. The methodologies reported in the literature usually assume that the information of the railway track is available; however, in some cases, the track information is incomplete, not available, or the route is still in a planning stage. Therefore, this paper proposes a methodology to estimate the energy consumption and the potential energy generation of a locomotive when the railway track information is not available or incomplete. The methodology begins by extracting the main technical information of the locomotive to be analyzed. Then, the route is traced on Google Earth with steps of 100 m and the obtained information is processed to generate the longitude, latitude, elevation, and distance of the points along the route. From such information, it is possible to generate the slope and curvature profiles, while the speed profile can be obtained from the track operator or the regulations of a specific country. With that information, it is possible to estimate the equivalent power of the locomotive at each point of the route to finally calculate the consumed energy. The proposed methodology is validated with two case studies. The first one compares the results with a methodology available in the literature for the same route and locomotive, while the second case shows the applicability of the proposed methodology for a route without information.

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Section: Calculation Of Mechanical Power and Electric Energy Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology for the Estimation of Electrical Power Consumed by Locomotives on Undocumented Railroad Tracks

et al. 2022

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“…To determine the set values of the monitored parameters wrelative predetermined, cosφ predetermined, and wlosses predetermined, it is necessary to make a mathematical model describing the dependence of these values on the factors accepted for consideration. Such a model can be specified both explicitly using a system of differential equations, and implicitly, for example, using an artificial neural network or a simulation model [9][10][11][12][13][14][15].…”

Section: Defining the Set Values Of The Monitored Indicators Based Onmentioning

confidence: 99%

Technology for reducing the consumption and losses of electrical energy in the power supply systems of railway consumers

et al. 2018

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The paper is devoted to the development of technology for reducing the consumption and losses of electrical energy of railway transport enterprises on the basis of the concept of "smart enterprise". A design solution to the monitoring and management system for energy efficiency indicators was proposed; monitored parameters and influencing factors were determined. The application of the simulation model in the MATLAB/Simulink program for determining the set values of the monitored system parameters, namely, power losses and power factor depending on the influencing factors, is considered. The approximating equation is obtained. Specific power losses were calculated for various control options.

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“…One of the ways to increase the energy efficiency of railway transport is the improvement of the methods of control, rationing, and management of the process of electricity consumption. This question implies the need to develop mathematical models of electric power consumption processes for train traction or stationary consumers [4]. Locomotive energy consumption refers to the amount of fuel consumed by locomotives throughout the process of locomotive routing, mainly including the consumption of traction trains, light locomotive running, shunting, and other situations [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…Locomotive energy consumption refers to the amount of fuel consumed by locomotives throughout the process of locomotive routing, mainly including the consumption of traction trains, light locomotive running, shunting, and other situations [5], [6]. Locomotive traction energy consumption accounts for 60%-70% of total railway energy consumption and is central to railway energy consumption [7], [8]. Reducing locomotive traction energy consumption is the focus of future railway energy saving and consumption reduction [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Analysis of Prediction Models for Locomotive Traction Energy Consumption Based on the Machine Learning

et al. 2023

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Locomotive traction energy consumption is a multivariate coupled nonlinear system closely related to many factors such as locomotive properties, routing, line conditions, and operating methods. In order to accurately calculate and predict locomotive traction energy consumption, three prediction models are constructed based on a large number of measured operation data of the HXD1 locomotive. This research uses two neural network methods of backpropagation and radial basis functions, as well as support vector machines. Among them, the training group uses 600 data; the validation group uses 200 data. The above methods can compare and analyze prediction performance between different neural networks and machine learning algorithm models. The results show that the RBF and BP neural networks can effectively predict locomotive traction energy consumption among the different neural network models. The R 2 of the two neural network model test groups is 0.9926 and 0.9885, the MAPE is 2.91% and 7.28%, and the MSE is 0.02% and 0.08%, respectively. Moreover, we have avoided the influence of the randomness of the neural network algorithm through repeated running. It shows that RBF neural network is better than BP neural network in predicting locomotive traction energy consumption, with more powerful approximation performance and higher accuracy. Among the different machine learning algorithms, the R 2 of the SVM algorithm model test group is 0.9983, the MAPE is 2.01%, and the MSE is 0.02%, which shows the prediction accuracy and overall performance of the SVM algorithm model is better than the neural network model. Finally, we prove the broad generalization of the SVM algorithm through the application on other lines. The SVM algorithm model can be a powerful tool for calculating and predicting the traction energy consumption of HXD1 locomotives.

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Construction of electricity consumption mathematical models on railway transport used artificial neural network and fuzzy neural network

Cited by 14 publications

References 10 publications

Methodology for the Estimation of Electrical Power Consumed by Locomotives on Undocumented Railroad Tracks

Methodology for the Estimation of Electrical Power Consumed by Locomotives on Undocumented Railroad Tracks

Technology for reducing the consumption and losses of electrical energy in the power supply systems of railway consumers

Comparison and Analysis of Prediction Models for Locomotive Traction Energy Consumption Based on the Machine Learning

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