Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Ma, Tai-Yu; Faye, Sébastien

doi:10.1016/j.energy.2022.123217

Cited by 71 publications

(22 citation statements)

References 27 publications

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“…Because of the difficulty in capturing multi-scale periodic features, there is still much room for improvement in the final accuracy. Ma (2021) proposed a mixed LSTM model to improve the prediction of charge stations occupancy [9]; Cheng (2020) uses mixed LSTM model to analysis the sentiment of language [10]. Both of them gained more remarkable results than single LSTM model by using mixed models.…”

Section: Discussionmentioning

confidence: 99%

The Research on the Prediction of Cryptocurrency Based on Linear Regression and LSTM

Ding¹

2023

Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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As the growing interest of investment on Cryptocurrencies and the huge volatility of their price, a need for scientific model to predict the future price is growing. In this context, the paper uses linear regression and LSTM model to predict the price of Bitcoin and Ethereum. The result shows that the prediction made by Linear Regression shows less errors but greater lag compared with the prediction made by LSTM method. The lag problem is considered to generate from lack of peripheral information other than previous prices. The prediction implies that the prices of Cryptocurrencies are theoretically predictable, and shows a direction of further research, such as the use of mixed-LSTM model. The methods provided in this paper can be used in development of better models and further investments.

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Section: Discussionmentioning

confidence: 99%

The Research on the Prediction of Cryptocurrency Based on Linear Regression and LSTM

Ding¹

2023

Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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“…The objective is to predict the parameters of the charging load profiles for a smart charging management system [ 18 ]. Existing studies mainly apply statistical models [ 19 ], such as Gaussian mixture models [ 18 ], and deep learning approaches [ 20 ], such as a hybrid LSTM neural network [ 21 , 22 ], to forecast charging loads at EV charging stations. In [ 23 ], the authors reviewed the most-popular techniques for EV load modeling, including deterministic and probabilistic methods.…”

Section: Related Researchmentioning

confidence: 99%

AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

Luo

Song

Huang

et al. 2023

Sensors

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Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines.

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“…For the large-scale uncertainties present in this type of integrated model, a data-driven approach integrating machine learning with optimization needs to be adapted [21]. Although DL approaches are used in public charging station occupancy prediction to reduce electric vehicle operator and user inconvenience [52], load forecasting [53], [54], [55], [56], their direct implementation for peak load management in an MG integrated with RE and EV transportation system cannot be found.…”

Section: Introductionmentioning

confidence: 99%

Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

2023

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Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO 2 emissions. Research shows that by 2030, EVs can reduce CO 2 emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours.INDEX TERMS CO 2 emission, data-driven approach, deep learning, demand-side management, electric vehicle charging station, peak clipping.

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Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Cited by 71 publications

References 27 publications

The Research on the Prediction of Cryptocurrency Based on Linear Regression and LSTM

The Research on the Prediction of Cryptocurrency Based on Linear Regression and LSTM

AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

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