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Predicting future loads of electric vehicles in the UK

Research output: Thesis › Master's Thesis

Published

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Predicting future loads of electric vehicles in the UK. / Roy, Rahul.
Lancaster University, 2020. 184 p.

Research output: Thesis › Master's Thesis

Harvard

Roy, R 2020, 'Predicting future loads of electric vehicles in the UK', Masters by Research, Lancaster University. https://doi.org/10.17635/lancaster/thesis/932

APA

Roy, R. (2020). Predicting future loads of electric vehicles in the UK. [Master's Thesis, Lancaster University]. Lancaster University. https://doi.org/10.17635/lancaster/thesis/932

Vancouver

Roy R. Predicting future loads of electric vehicles in the UK. Lancaster University, 2020. 184 p. doi: 10.17635/lancaster/thesis/932

Author

Roy, Rahul. / Predicting future loads of electric vehicles in the UK. Lancaster University, 2020. 184 p.

Bibtex

@mastersthesis{9e4a423d49544b428a122805187bba1d,
title = "Predicting future loads of electric vehicles in the UK",
abstract = "This thesis aims to propose a robust statistical model to predict the future energy demand on low voltage distribution networks based on the data obtained from the EV (electric vehicle) trials of Electric Nation project, conducted from 2017 to 2018. While the ultimate objective of Electric Nation is to assess the impact of EV charging on distribution networks and enable the distribution network operators (DNOs) to make informed decisions on demand management, this research project, as part of Electric Nation, aims to build relevant statistical models that would help the industry partner, EA Technology, to forecast the quantum of energy consumption, with high accuracy, that EV charging would lead to. In current research, we develop four statistical models based on four different algorithms: we start with time series regression as the benchmark model and iteratively improve the forecast accuracy of the benchmark model by boosting methodology. In addition, we also explore deep learning models (LSTM networks as the data is sequential) and identify that such models, with little hyperparameter tuning, deliver the best forecast accuracy among all the models.",
keywords = "electric vehicle (EV); EV charging; time series; forecasting; regression; ARIMA; LSTM networks.",
author = "Rahul Roy",
year = "2020",
month = feb,
day = "19",
doi = "10.17635/lancaster/thesis/932",
language = "English",
publisher = "Lancaster University",
school = "Lancaster University",

}

RIS

TY - GEN

T1 - Predicting future loads of electric vehicles in the UK

AU - Roy, Rahul

PY - 2020/2/19

Y1 - 2020/2/19

N2 - This thesis aims to propose a robust statistical model to predict the future energy demand on low voltage distribution networks based on the data obtained from the EV (electric vehicle) trials of Electric Nation project, conducted from 2017 to 2018. While the ultimate objective of Electric Nation is to assess the impact of EV charging on distribution networks and enable the distribution network operators (DNOs) to make informed decisions on demand management, this research project, as part of Electric Nation, aims to build relevant statistical models that would help the industry partner, EA Technology, to forecast the quantum of energy consumption, with high accuracy, that EV charging would lead to. In current research, we develop four statistical models based on four different algorithms: we start with time series regression as the benchmark model and iteratively improve the forecast accuracy of the benchmark model by boosting methodology. In addition, we also explore deep learning models (LSTM networks as the data is sequential) and identify that such models, with little hyperparameter tuning, deliver the best forecast accuracy among all the models.

AB - This thesis aims to propose a robust statistical model to predict the future energy demand on low voltage distribution networks based on the data obtained from the EV (electric vehicle) trials of Electric Nation project, conducted from 2017 to 2018. While the ultimate objective of Electric Nation is to assess the impact of EV charging on distribution networks and enable the distribution network operators (DNOs) to make informed decisions on demand management, this research project, as part of Electric Nation, aims to build relevant statistical models that would help the industry partner, EA Technology, to forecast the quantum of energy consumption, with high accuracy, that EV charging would lead to. In current research, we develop four statistical models based on four different algorithms: we start with time series regression as the benchmark model and iteratively improve the forecast accuracy of the benchmark model by boosting methodology. In addition, we also explore deep learning models (LSTM networks as the data is sequential) and identify that such models, with little hyperparameter tuning, deliver the best forecast accuracy among all the models.

KW - electric vehicle (EV); EV charging; time series; forecasting; regression; ARIMA; LSTM networks.

U2 - 10.17635/lancaster/thesis/932

DO - 10.17635/lancaster/thesis/932

M3 - Master's Thesis

PB - Lancaster University

ER -