Incomplete data in smart grid: Treatment of missing values in electric vehicle charging data

Majidpour, Mostafa; Chu, Pao-Hsien; Gadh, Rajit; Pota, H. R.

doi:10.1109/iccve.2014.7297505

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…C6. Power quality power quality disturbances classification ( [22], [33], [34], [37], [63], [73], [104], [121], [155], [170], [215]), power data compression ( [55], [59], [71], [133], [134], [140], [181], [192], [231], [244], [246]), meter placement for quality estimation ( [1], [9]), energy losses detection ( [38]), missing data imputation [177], [205] C7. Pricing pricing forecasting ( [5], [186], [188], [189], [200], [222]- [225], [229], [243], [249]), pricing impact on customer behaviour ( [27], [241]), pricing for demand-side management ( [91], [107])…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Some imputation methods involving deletion, such as "case deletion" where the incomplete instance of data is removed from the dataset, are not suitable for time series since they will change the relative order of events and make the time series lose its ordinal properties such as periodicity. A more elaborate discussion on imputation methods and their application on energy time series have been discussed in [35]. The article argues that each prediction algorithm goes along well with a certain imputation method and care should be taken in selecting an imputation method for each prediction algorithm.…”

Section: A Station Recordsmentioning

confidence: 99%

Forecasting the EV charging load based on customer profile or station measurement?

et al. 2016

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In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two different datasets: data from the customer profile (referred to as charging record) and data from outlet measurements (referred to as station record). Four different prediction algorithms namely Time Weighted Dot Product based Nearest Neighbor (TWDP-NN), Modified Pattern Sequence Forecasting (MPSF), Support Vector Regression (SVR), and Random Forest (RF) are applied to both datasets. The corresponding speed, accuracy, and privacy concerns are compared between the use of the charging records and station records. Real world data compiled at the outlet level from the UCLA campus parking lots are used. The results show that charging records provide relatively faster prediction while putting customer privacy in jeopardy. Station records provide relatively slower prediction while respecting the customer privacy. In general, we found that both datasets generate comparable prediction error.

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“…We found only one study addressing the missing value issue in EV data. In Reference [9], the authors conducted an experiment to process missing values of EV data using univariate and multivariate imputation techniques. As stated in Reference [9], in most missing values cases of the dataset, all values of that instance are missing.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [9], the authors conducted an experiment to process missing values of EV data using univariate and multivariate imputation techniques. As stated in Reference [9], in most missing values cases of the dataset, all values of that instance are missing. Therefore, the results show that the multivariate imputation techniques could not estimate reasonable values, and only univariate techniques such as median and zero imputations improve the prediction performance slightly.…”

Section: Introductionmentioning

confidence: 99%

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Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Lee

Ahn

2020

Energies

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As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

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Incomplete data in smart grid: Treatment of missing values in electric vehicle charging data

Cited by 10 publications

References 4 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Forecasting the EV charging load based on customer profile or station measurement?

Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

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