A Sensory Similarities Approach to Load Disaggregation of Charging Stations in Internet of Electric Vehicles

Liu, Qi; Kamoto, Kondwani Michael; Liu, Xiaodong; Zhang, Yonghong; Yang, Zhong-Jian; Khosravi, Mohammad R.; Xu, Yanwei; Qi, Lianyong

doi:10.1109/jsen.2020.3027684

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…The mainstream influencing factors are: season, temperature, number of users, and house location. 62,63 Among these consideration factors, few people consider the relationship between houses and houses. This model starts from the relationship between the house A and the house B, and generates the corresponding topological structure network for the relationship between the house A and the house B.…”

Section: Graph Generation Methods For Spatial Correlationmentioning

confidence: 99%

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A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

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Section: Graph Generation Methods For Spatial Correlationmentioning

confidence: 99%

“…For the feature extraction of load power data, researchers will consider the various influencing factors on the load power. The mainstream influencing factors are: season, temperature, number of users, and house location 62,63 . Among these consideration factors, few people consider the relationship between houses and houses.…”

Section: Experiments Evaluationmentioning

confidence: 99%

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [28] a framework was developed for disaggregating the total energy of plug-in electric vehicles at the feeder head level. A different article [29] modelled the electricity consumption of charging stations, using similarities between the created models to better understand the future requirements of similar charging stations. Article [30] disaggregated feeder head-level consumption into separate smart meter readings, encompassing both industrial and residential buildings.…”

Section: Fig 1 the Investigated Domains In The Literaturementioning

confidence: 99%

A Scoping Review of Energy Load Disaggregation

Tolnai,

Ma,

Jørgensen

2023

Lecture Notes in Computer Science

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Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

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“…At present, with the development of the internet of vehicles technology [20], car owners can intuitively obtain various types of information through mobile phone apps before and during the journey, such as congestion of route, electricity price of EVCSs, and even the queuing situation. All this information will exert a great influence on the decision-making scenario of EV owners in choosing charging places, thus affecting the distribution of charging loads to a certain extent.…”

Section: Framework Of Evcs Load Forecasting Model Based On Multi-sour...mentioning

confidence: 99%

Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification

et al. 2022

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In view of the current multi-source information scenario, this paper proposes a decision-making method for electric vehicle charging stations (EVCSs) based on prospect theory, which considers payment cost, time cost, and route factors, and is used for electric vehicle (EV) owners to make decisions when the vehicle’s electricity is low. Combined with the multi-source information architecture composed of an information layer, algorithm layer, and model layer, the load of EVCSs in the region is forecast. In this paper, the Monte Carlo method is used to test the IEEE-30 model and the traffic network based on it, and the spatial and temporal distribution of charging load in the region is obtained, which verifies the effectiveness of the proposed method. The results show that EVCS load forecasting based on the prospect theory under the influence of multi-source information will have an impact on the space–time distribution of the EVCS load, which is more consistent with the decisions of EV owners in reality.

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A Sensory Similarities Approach to Load Disaggregation of Charging Stations in Internet of Electric Vehicles

Cited by 9 publications

References 43 publications

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A Scoping Review of Energy Load Disaggregation

Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification

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