Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning
The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/5/1747 |
| _version_ | 1797475224442109952 |
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| author | Teresa Pamuła Danuta Pamuła |
| author_facet | Teresa Pamuła Danuta Pamuła |
| author_sort | Teresa Pamuła |
| collection | DOAJ |
| description | The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses. |
| first_indexed | 2024-03-09T20:42:07Z |
| format | Article |
| id | doaj.art-bbd61c6c2d7149188f5e004782889aa0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:42:07Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bbd61c6c2d7149188f5e004782889aa02023-11-23T22:56:55ZengMDPI AGEnergies1996-10732022-02-01155174710.3390/en15051747Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep LearningTeresa Pamuła0Danuta Pamuła1Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, PolandRockwell Automation, 40-382 Katowice, PolandThe energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.https://www.mdpi.com/1996-1073/15/5/1747energy predictionelectric busesdeep learningurban bus network |
| spellingShingle | Teresa Pamuła Danuta Pamuła Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning Energies energy prediction electric buses deep learning urban bus network |
| title | Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning |
| title_full | Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning |
| title_fullStr | Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning |
| title_full_unstemmed | Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning |
| title_short | Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning |
| title_sort | prediction of electric buses energy consumption from trip parameters using deep learning |
| topic | energy prediction electric buses deep learning urban bus network |
| url | https://www.mdpi.com/1996-1073/15/5/1747 |
| work_keys_str_mv | AT teresapamuła predictionofelectricbusesenergyconsumptionfromtripparametersusingdeeplearning AT danutapamuła predictionofelectricbusesenergyconsumptionfromtripparametersusingdeeplearning |