Electric vehicle charging demand forecasting using deep learning model

Yi, Zhiyan; Liu, Cathy; Wei, Ran; Chen, Xi; Dai, Jiangpeng

doi:10.1080/15472450.2021.1966627

Cited by 69 publications

(18 citation statements)

References 37 publications

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“…Nevertheless, they represent a great foundation for forecasting EV charging load. On the other hand, EV-related studies [10,[24][25][26][27][28][29] do consider EV charging but they do so for a group of EVs, parking lots, charging-station, or regions, and do not confider forecasting load for individual households in presence of EVs. In contrast, we focus on predicting power consumption for individual households in presence of EV charging.…”

Section: Electricity Load Forecastingmentioning

confidence: 99%

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

Skala,

Elgalhud,

Grolinger

et al. 2023

Energies

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The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

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Section: Electricity Load Forecastingmentioning

confidence: 99%

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

Skala,

Elgalhud,

Grolinger

et al. 2023

Energies

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“…In [68], a DL-based forecasting method called Sequence to Sequence was developed for predicting EVEC up to one month and five months in advance. The effectiveness of this approach was tested on real-world data from 1200 charging stations in Los Angeles.…”

Section: ) Deep Learning (Dl)mentioning

confidence: 99%

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Marzbani,

Osman,

Hassan

2023

IEEE Access

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Accurate forecast of electric vehicle energy demand is vital for maintaining the stability and reliability of power systems. With the increasing prevalence of electric vehicles in transportation systems, the anticipating demand surges with precision in terms of timing and location is becoming ever more critical for utilities to guarantee sufficient supply. The intermittent and stochastic nature of electric vehicle electricity consumption is a significant challenge in accurately forecasting of electric vehicles demand. As a result, there is a growing field of research focused on developing models that can effectively capture and interpret such complex data. In improving the potential of accurate prediction models, conducting a comprehensive review of literature, examining current research overviews, and exploring potential expansions and extensions of models are all critical components. In this review, a comprehensive overview of prior research conducted for forecasting electric vehicle energy demand is presented, including a detailed examination of the benefits and drawbacks of the methods used. Additionally, potential gaps in the field are identified, and recommendations for future research directions are provided.

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“…Effective forecasting of commercial EV bill demand ensures the reliability and stability of short-term network utilities and supports investment planning and resource allocation for long-term infrastructure bills. e article by Yi et al [17] provides an overview of the monthly commercial EV load application time series using the deep learning approach. e proposed model was confirmed by original datasets in Utah and Los Angeles.…”

Section: E-mobility Data Analysis Using Deep Learningmentioning

confidence: 99%

Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor

Park

Lee

Byun

2022

Journal of Advanced Transportation

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Demand for electric kickboards is increasing specifically in tourist-centric regions worldwide. In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want. However, it is necessary to study how to divide the region to predict electric mobility demand by region. Therefore, this study is made to more accurately predict future demand based on past regional customers’ electric mobility demand data. We have proposed a novel electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor. We have used electric kickboard usage data from a Jeju, South Korea-based company. As a result of the experiment, it was found that the accuracy before using clustering-based bagging regressor and when the region was divided by the clustering method, the performance was improved, and we have achieved a regression score R 2 of 93.42 using our proposed approach. We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors. This study can be helpful for companies to meet the user’s demand for a better quality of service.

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Electric vehicle charging demand forecasting using deep learning model

Cited by 69 publications

References 37 publications

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor

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