Research Article
BibTex RIS Cite

Year 2023, , 10 - 21, 30.04.2023

Pinar Cihan

https://doi.org/10.35377/saucis...1209519
Cited By: 1

Abstract

References

  • [1] E. P. Agency, “Inventory of US greenhouse gas emissions and sinks: 1990-2005,” ed: United States Environment Protection Agency, 2005.
  • [2] X. Zheng, D. Streimikiene, T. Balezentis, A. Mardani, F. Cavallaro, H. Liao, “A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players,” Journal of Cleaner Production, vol. 234, pp. 1113-1133, 2019.
  • [3] Z. Yi, X. C. Liu, R. Wei, X. Chen, J. Dai, “Electric vehicle charging demand forecasting using deep learning model,” Journal of Intelligent Transportation Systems, vol.26, no.6, pp. 1-14, 2021.
  • [4] E. Xydas, C. Marmaras, L. M. Cipcigan, A. S. Hassan, N. Jenkins, “Forecasting electric vehicle charging demand using support vector machines,” in 2013 48th International Universities' Power Engineering Conference (UPEC), pp. 1-6, 2013.
  • [5] H. Li, Z. Wan, H. He, “Constrained EV charging scheduling based on safe deep reinforcement learning,” IEEE Transactions on Smart Grid. vol. 11, no. 3 pp. 2427-2439, 2019.
  • [6] Global EV Outlook, “OECD/IEA,” [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2018. [Accessed: 22- Sep-2021].
  • [7] Global EV Outlook, “Trends and developments in electric vehicle markets,” [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets. [Accessed: 22- Sep-2021].
  • [8] E. Muehlegger, D. Rapson, “Subsidizing mass adoption of electric vehicles: Quasi-experimental evidence from California,” NBER Working Paper, 2018.
  • [9] Y. Amara-Ouali, Y. Goude, P. Massart, J. M. Poggi, H. Yan, “A review of electric vehicle load open data and models,” Energies, vol. 14, no. 3, p. 2233, 2021.
  • [10] T. Unterluggauer, K. Rauma, P. Järventausta, C. Rehtanz, “Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland,” IET Electrical Systems in Transportation, vol. 11, pp. 405-419, 2021.
  • [11] Z. Na, T. HanZhen, L. YuTong, C. Jia, Y. JunYou, G. Wang, “Short-term load forecasting algorithm based on LSTM-DBN considering the flexibility of electric vehicle,” in IOP Conference Series: Earth and Environmental Science, 2020, 042001.
  • [12] J. Huber, D. Dann, C. Weinhardt, “Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging,” Applied Energy, vol. 262, p. 114525, 2020.
  • [13] X. Zhang, K. W. Chan, H. Li, H. Wang, J. Qiu, G. Wang, “Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model,” IEEE transactions on cybernetics, vol. 51, no. 6, pp. 3157-3170, 2020.
  • [14] J. Zhu, Z. Yang, M. Mourshed, Y. Guo, Y. Zhou, Y. Chang, Y. Wei, S. Feng, “Electric vehicle charging load forecasting: A comparative study of deep learning approaches,” Energies, vol. 12, p. 2692, 2019.
  • [15] J. Zhu, Z. Yang, Y. Guo, J. Zhang, H. Yang, “Short-term load forecasting for electric vehicle charging stations based on deep learning approaches,” Applied sciences, vol. 9, p. 1723, 2019.
  • [16] T. H. C. Tat and P. Fränti, “Real-time Electric Vehicle Load Forecast to Meet Timely Energy Dispatch,” in 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 148-153, 2018.
  • [17] H. M. Louie, “Time-series modeling of aggregated electric vehicle charging station load,” Electric Power Components and Systems, vol. 45, pp. 1498-1511, 2017.
  • [18] M. H. Amini, A. Kargarian, O. Karabasoglu, “ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation,” Electric Power Systems Research, vol. 140, pp. 378-390, 2016.
  • [19] M. Majidpour, C. Qiu, P. Chu, H.R. Pota, R. Gadh, “Forecasting the EV charging load based on customer profile or station measurement?,” Applied energy, vol. 163, pp. 134-141, 2016.
  • [20] M. Majidpour, C. Qiu, P., Chu, R., Gadh, H. R. Pota, “A novel forecasting algorithm for electric vehicle charging stations,” in 2014 International Conference on Connected Vehicles and Expo (ICCVE), pp. 1035-1040, 2014.
  • [21] City Of Edmonton, “Public Charging Stations for Electric Vehicles,” [Online]. Available: data.edmonton.ca. [Accessed: 22- Sep-2021].
  • [22] NOBIL, “NOBIL Database,” [Online]. Available: info.nobil.no/eng. [Accessed: 22- Sep-2021].
  • [23] Data Gouv, “Charging Sessions Apr–May 2017 in Paris,” [Online]. Available: www.data.gouv.fr. [Accessed: 22- Sep-2021].
  • [24] Paris Data, “Belib’ Availability in Real-Time,” [Online]. Available: opendata.paris.fr. [Accessed: 22- Sep-2021].
  • [25] Data Bonn, “Charging Point Locations and Usage in Real-Time in Bonn,” [Online]. Available: opendata.bonn.de. [Accessed: 22- Sep-2021].
  • [26] Elaad NL, “Data Analytics,” www.elaad.nl/research/data-analytics/. [Accessed: 22- Sep-2021].
  • [27] Rotterdam Open Data, “Charging Point Locations and Usage in Rotterdam,” [Online]. Available: https://rotterdamopendata.nl/#/data. [Accessed: 22- Sep-2021].
  • [28] Elbil Sverige, “Charging Point Locations in Nordic Countries,” [Online]. Available: www.elbilsverige.se. [Accessed: 22- Sep-2021].
  • [29] Transport Team, “Electric Vehicle Charging Sessions Dundee,” [Online]. Available: data.dundeecity.gov.uk. [Accessed: 22- Sep-2021].
  • [30] OpenData Team, “Electric Vehicle Charging Station Usage in Perth and Kinross,” [Online]. Available: data.pkc.gov.uk. [Accessed: 22- Sep-2021].
  • [31] L. Makram, “Electric Vehicle Charging Stations: Energy Consumption & Savings,” [Online]. Available: open-data.bouldercolorado.gov. [Accessed: 22- Sep-2021].
  • [32] City of Palo Alto, “Electric Vehicle Charging Station Usage,” [Online]. Available: data.cityofpaloalto.org. [Accessed: 22- Sep-2021].
  • [33] City of Evanston, “City-owned Electric Vehicle Charging Station Usage,” [Online]. Available: data.cityofevanston.org. A[Accessed: 22- Sep-2021].
  • [34] ACN-Data, “A Public EV Charging Dataset,” [Online]. Available: ev.caltech.edu/dataset. [Accessed: 22- Sep-2021].
  • [35] V. Vapnik, “The nature of statistical learning theory,” Springer science & business media, 1999.
  • [36] M. O. Elish, “A comparative study of fault density prediction in aspect-oriented systems using MLP, RBF, KNN, RT, DENFIS and SVR models,” Artificial Intelligence Review, vol. 42, pp. 695-703, 2014.
  • [37] A. J. Smola, B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing, vol. 14, pp. 199-222, 2004.
  • [38] L. Breiman, “ Random forests,” Machine learning, vol. 45, pp. 5-32, 2001.
  • [39] D. J. MacKay, “Introduction to Gaussian processes,” NATO ASI series F computer and systems sciences, vol. 168, pp. 133-166, 1998.
  • [40] J. Zurada, “Introduction to artificial neural systems,” West Publishing Co., 1992.
  • [41] P, Cihan. “Fuzzy rule-based system for predicting daily case in covid-19 outbreak.” 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2020. https://doi.org/10.1109/ISMSIT50672.2020.9254714
  • [42] P, Cihan and O, Kalipsiz, “Öğrenci proje anketlerini sınıflandırmada en iyi algoritmanın belirlenmesi.” Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, vol. 8, no. 1, pp. 41-49, 2016.
  • [43] P, Cihan, H, Ozel, and H. K. Ozcan, “Modeling of atmospheric particulate matters via artificial intelligence methods.” Environmental Monitoring and Assessment, vol. 193, no. 5, pp. 1-15, 2021. https://doi.org/10.1007/s10661-021-09091-1
  • [44] R. Van Den Hoed, J. Helmus, R. De Vries, D. Bardok, “Data analysis on the public charge infrastructure in the city of Amsterdam,” World Electric Vehicle Journal, vol. 6, pp. 829-838, 2013.
  • [45] J. C. Spoelstra and I. J. Helmus, “Public charging infrastructure use in the Netherlands: A rollout-strategy assessment,” in Proc. of European Battery, Hybrid and Fuel Cell Electric Vehicle Congress, 2016.
  • [46] N. Sathaye and S. Kelley, “ An approach for the optimal planning of electric vehicle infrastructure for highway corridors,” Transportation Research Part E: Logistics and Transportation Review, vol. 59, pp. 15-33, 2013.
  • [47] J. Liu, “Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing,” Energy policy, vol. 51, pp. 544-557, 2012.

Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand

Year 2023, , 10 - 21, 30.04.2023

Pinar Cihan

https://doi.org/10.35377/saucis...1209519
Cited By: 1

Abstract

The increase in environmental problems such as climate change and air pollution caused by global warming has risen the popularity of electric vehicles (EVs) used in the smart grid environment. The increasing number of EVs can affect the grid in terms of power loss and voltage bias by changing the existing demand profile. Effective predicting of EVs energy demand ensures reliability and robustness of grid use, as well as aiding investment planning and resource allocation for charging infrastructures. In this study, the electricity demand amounts in two different cities are modeled by Support Vector Regression, Random Forest, Gauss Process, and Multilayer Perceptron algorithms. The findings reveal that electric vehicle owners usually start to charge their vehicles during the daytime, the COVID-19 pandemic causes a serious decrease in EVs energy demand, and the support vector regression (SVR) is more successful in energy demand forecasting. Furthermore, the results indicate that the decrease in electricity demand during the COVID-19 pandemic caused reduces in the prediction accuracy of the SVR model (decrease of 17.1% in training and 12.6% in test performance, P<0.001).

Keywords

electric vehicles energy demand time series machine learning COVID-19

References

  • [1] E. P. Agency, “Inventory of US greenhouse gas emissions and sinks: 1990-2005,” ed: United States Environment Protection Agency, 2005.
  • [2] X. Zheng, D. Streimikiene, T. Balezentis, A. Mardani, F. Cavallaro, H. Liao, “A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players,” Journal of Cleaner Production, vol. 234, pp. 1113-1133, 2019.
  • [3] Z. Yi, X. C. Liu, R. Wei, X. Chen, J. Dai, “Electric vehicle charging demand forecasting using deep learning model,” Journal of Intelligent Transportation Systems, vol.26, no.6, pp. 1-14, 2021.
  • [4] E. Xydas, C. Marmaras, L. M. Cipcigan, A. S. Hassan, N. Jenkins, “Forecasting electric vehicle charging demand using support vector machines,” in 2013 48th International Universities' Power Engineering Conference (UPEC), pp. 1-6, 2013.
  • [5] H. Li, Z. Wan, H. He, “Constrained EV charging scheduling based on safe deep reinforcement learning,” IEEE Transactions on Smart Grid. vol. 11, no. 3 pp. 2427-2439, 2019.
  • [6] Global EV Outlook, “OECD/IEA,” [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2018. [Accessed: 22- Sep-2021].
  • [7] Global EV Outlook, “Trends and developments in electric vehicle markets,” [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets. [Accessed: 22- Sep-2021].
  • [8] E. Muehlegger, D. Rapson, “Subsidizing mass adoption of electric vehicles: Quasi-experimental evidence from California,” NBER Working Paper, 2018.
  • [9] Y. Amara-Ouali, Y. Goude, P. Massart, J. M. Poggi, H. Yan, “A review of electric vehicle load open data and models,” Energies, vol. 14, no. 3, p. 2233, 2021.
  • [10] T. Unterluggauer, K. Rauma, P. Järventausta, C. Rehtanz, “Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland,” IET Electrical Systems in Transportation, vol. 11, pp. 405-419, 2021.
  • [11] Z. Na, T. HanZhen, L. YuTong, C. Jia, Y. JunYou, G. Wang, “Short-term load forecasting algorithm based on LSTM-DBN considering the flexibility of electric vehicle,” in IOP Conference Series: Earth and Environmental Science, 2020, 042001.
  • [12] J. Huber, D. Dann, C. Weinhardt, “Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging,” Applied Energy, vol. 262, p. 114525, 2020.
  • [13] X. Zhang, K. W. Chan, H. Li, H. Wang, J. Qiu, G. Wang, “Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model,” IEEE transactions on cybernetics, vol. 51, no. 6, pp. 3157-3170, 2020.
  • [14] J. Zhu, Z. Yang, M. Mourshed, Y. Guo, Y. Zhou, Y. Chang, Y. Wei, S. Feng, “Electric vehicle charging load forecasting: A comparative study of deep learning approaches,” Energies, vol. 12, p. 2692, 2019.
  • [15] J. Zhu, Z. Yang, Y. Guo, J. Zhang, H. Yang, “Short-term load forecasting for electric vehicle charging stations based on deep learning approaches,” Applied sciences, vol. 9, p. 1723, 2019.
  • [16] T. H. C. Tat and P. Fränti, “Real-time Electric Vehicle Load Forecast to Meet Timely Energy Dispatch,” in 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 148-153, 2018.
  • [17] H. M. Louie, “Time-series modeling of aggregated electric vehicle charging station load,” Electric Power Components and Systems, vol. 45, pp. 1498-1511, 2017.
  • [18] M. H. Amini, A. Kargarian, O. Karabasoglu, “ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation,” Electric Power Systems Research, vol. 140, pp. 378-390, 2016.
  • [19] M. Majidpour, C. Qiu, P. Chu, H.R. Pota, R. Gadh, “Forecasting the EV charging load based on customer profile or station measurement?,” Applied energy, vol. 163, pp. 134-141, 2016.
  • [20] M. Majidpour, C. Qiu, P., Chu, R., Gadh, H. R. Pota, “A novel forecasting algorithm for electric vehicle charging stations,” in 2014 International Conference on Connected Vehicles and Expo (ICCVE), pp. 1035-1040, 2014.
  • [21] City Of Edmonton, “Public Charging Stations for Electric Vehicles,” [Online]. Available: data.edmonton.ca. [Accessed: 22- Sep-2021].
  • [22] NOBIL, “NOBIL Database,” [Online]. Available: info.nobil.no/eng. [Accessed: 22- Sep-2021].
  • [23] Data Gouv, “Charging Sessions Apr–May 2017 in Paris,” [Online]. Available: www.data.gouv.fr. [Accessed: 22- Sep-2021].
  • [24] Paris Data, “Belib’ Availability in Real-Time,” [Online]. Available: opendata.paris.fr. [Accessed: 22- Sep-2021].
  • [25] Data Bonn, “Charging Point Locations and Usage in Real-Time in Bonn,” [Online]. Available: opendata.bonn.de. [Accessed: 22- Sep-2021].
  • [26] Elaad NL, “Data Analytics,” www.elaad.nl/research/data-analytics/. [Accessed: 22- Sep-2021].
  • [27] Rotterdam Open Data, “Charging Point Locations and Usage in Rotterdam,” [Online]. Available: https://rotterdamopendata.nl/#/data. [Accessed: 22- Sep-2021].
  • [28] Elbil Sverige, “Charging Point Locations in Nordic Countries,” [Online]. Available: www.elbilsverige.se. [Accessed: 22- Sep-2021].
  • [29] Transport Team, “Electric Vehicle Charging Sessions Dundee,” [Online]. Available: data.dundeecity.gov.uk. [Accessed: 22- Sep-2021].
  • [30] OpenData Team, “Electric Vehicle Charging Station Usage in Perth and Kinross,” [Online]. Available: data.pkc.gov.uk. [Accessed: 22- Sep-2021].
  • [31] L. Makram, “Electric Vehicle Charging Stations: Energy Consumption & Savings,” [Online]. Available: open-data.bouldercolorado.gov. [Accessed: 22- Sep-2021].
  • [32] City of Palo Alto, “Electric Vehicle Charging Station Usage,” [Online]. Available: data.cityofpaloalto.org. [Accessed: 22- Sep-2021].
  • [33] City of Evanston, “City-owned Electric Vehicle Charging Station Usage,” [Online]. Available: data.cityofevanston.org. A[Accessed: 22- Sep-2021].
  • [34] ACN-Data, “A Public EV Charging Dataset,” [Online]. Available: ev.caltech.edu/dataset. [Accessed: 22- Sep-2021].
  • [35] V. Vapnik, “The nature of statistical learning theory,” Springer science & business media, 1999.
  • [36] M. O. Elish, “A comparative study of fault density prediction in aspect-oriented systems using MLP, RBF, KNN, RT, DENFIS and SVR models,” Artificial Intelligence Review, vol. 42, pp. 695-703, 2014.
  • [37] A. J. Smola, B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing, vol. 14, pp. 199-222, 2004.
  • [38] L. Breiman, “ Random forests,” Machine learning, vol. 45, pp. 5-32, 2001.
  • [39] D. J. MacKay, “Introduction to Gaussian processes,” NATO ASI series F computer and systems sciences, vol. 168, pp. 133-166, 1998.
  • [40] J. Zurada, “Introduction to artificial neural systems,” West Publishing Co., 1992.
  • [41] P, Cihan. “Fuzzy rule-based system for predicting daily case in covid-19 outbreak.” 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2020. https://doi.org/10.1109/ISMSIT50672.2020.9254714
  • [42] P, Cihan and O, Kalipsiz, “Öğrenci proje anketlerini sınıflandırmada en iyi algoritmanın belirlenmesi.” Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, vol. 8, no. 1, pp. 41-49, 2016.
  • [43] P, Cihan, H, Ozel, and H. K. Ozcan, “Modeling of atmospheric particulate matters via artificial intelligence methods.” Environmental Monitoring and Assessment, vol. 193, no. 5, pp. 1-15, 2021. https://doi.org/10.1007/s10661-021-09091-1
  • [44] R. Van Den Hoed, J. Helmus, R. De Vries, D. Bardok, “Data analysis on the public charge infrastructure in the city of Amsterdam,” World Electric Vehicle Journal, vol. 6, pp. 829-838, 2013.
  • [45] J. C. Spoelstra and I. J. Helmus, “Public charging infrastructure use in the Netherlands: A rollout-strategy assessment,” in Proc. of European Battery, Hybrid and Fuel Cell Electric Vehicle Congress, 2016.
  • [46] N. Sathaye and S. Kelley, “ An approach for the optimal planning of electric vehicle infrastructure for highway corridors,” Transportation Research Part E: Logistics and Transportation Review, vol. 59, pp. 15-33, 2013.
  • [47] J. Liu, “Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing,” Energy policy, vol. 51, pp. 544-557, 2012.
There are 47 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Pinar Cihan 0000-0001-7958-7251

Early Pub Date April 28, 2023
Publication Date April 30, 2023
Submission Date November 24, 2022
Acceptance Date January 19, 2023
Published in Issue Year 2023

Cite

IEEE P. Cihan, “Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand”, SAUCIS, vol. 6, no. 1, pp. 10–21, 2023, doi: 10.35377/saucis...1209519.

Cited By

A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques
IEEE Open Journal of Vehicular Technology
https://doi.org/10.1109/OJVT.2024.3457499

29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License