Modeling and Forecasting Self-Similar Power Load Due to EV Fast Chargers
In this paper, we consider modeling and prediction of power loads due to fast charging stations for plug-in electric vehicles (EV). The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes t...
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| Veröffentlicht in: | IEEE transactions on smart grid 2016-05, Vol.7 (3), p.1620-1629, Article 1620 |
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| container_title | IEEE transactions on smart grid |
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| creator | Korolko, Nikita Sahinoglu, Zafer Nikovski, Daniel |
| description | In this paper, we consider modeling and prediction of power loads due to fast charging stations for plug-in electric vehicles (EV). The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes the time series obtained by this simulator and its properties. We show that the power load aggregated over a number of fast chargers (after deseasonalizing and elimination of the linear trend) is a self-similar process with the Hurst parameter 0.57 |
| doi_str_mv | 10.1109/TSG.2015.2458852 |
| format | Article |
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The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes the time series obtained by this simulator and its properties. We show that the power load aggregated over a number of fast chargers (after deseasonalizing and elimination of the linear trend) is a self-similar process with the Hurst parameter 0.57 <; H <; 0.67, where H varies depending on the multiplexing level. The main contribution of this paper is empirical evidence that a fitted fractional autoregressive integrated moving average (fARIMA) model taking into account self-similarity of the load time series can yield high-quality short-term forecasts when H is large enough. Namely, the fitted fARIMA model uniformly outperforms regular ARIMA algorithms in terms of root-mean-square error for predictions with time horizon up to 120 min for H ≥ 0.639. Moreover, we show that the fARIMA advantage on average grows as a function of the Hurst exponent H. Computational experiments demonstrate that this edge is stably greater than 1.1% and can be as high as 5-7% for some scenarios.</description><identifier>ISSN: 1949-3053</identifier><identifier>EISSN: 1949-3061</identifier><identifier>DOI: 10.1109/TSG.2015.2458852</identifier><identifier>CODEN: ITSGBQ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Aggregates ; Automotive components ; Computer simulation ; Demand forecasting ; Electric power generation ; Electric vehicles ; Forecasting ; Fractals ; Load modeling ; Mathematical models ; Modelling ; Predictive models ; Regression analysis ; Self-similarity ; System-on-chip ; Time series ; Time series analysis</subject><ispartof>IEEE transactions on smart grid, 2016-05, Vol.7 (3), p.1620-1629, Article 1620</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-fd68cbd67d7b803361fc7e0a9565eab26c89a7d6072d249bc838b547e8484d313</citedby><cites>FETCH-LOGICAL-c324t-fd68cbd67d7b803361fc7e0a9565eab26c89a7d6072d249bc838b547e8484d313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7182352$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7182352$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Korolko, Nikita</creatorcontrib><creatorcontrib>Sahinoglu, Zafer</creatorcontrib><creatorcontrib>Nikovski, Daniel</creatorcontrib><title>Modeling and Forecasting Self-Similar Power Load Due to EV Fast Chargers</title><title>IEEE transactions on smart grid</title><addtitle>TSG</addtitle><description>In this paper, we consider modeling and prediction of power loads due to fast charging stations for plug-in electric vehicles (EV). The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes the time series obtained by this simulator and its properties. We show that the power load aggregated over a number of fast chargers (after deseasonalizing and elimination of the linear trend) is a self-similar process with the Hurst parameter 0.57 <; H <; 0.67, where H varies depending on the multiplexing level. The main contribution of this paper is empirical evidence that a fitted fractional autoregressive integrated moving average (fARIMA) model taking into account self-similarity of the load time series can yield high-quality short-term forecasts when H is large enough. Namely, the fitted fARIMA model uniformly outperforms regular ARIMA algorithms in terms of root-mean-square error for predictions with time horizon up to 120 min for H ≥ 0.639. Moreover, we show that the fARIMA advantage on average grows as a function of the Hurst exponent H. Computational experiments demonstrate that this edge is stably greater than 1.1% and can be as high as 5-7% for some scenarios.</description><subject>Aggregates</subject><subject>Automotive components</subject><subject>Computer simulation</subject><subject>Demand forecasting</subject><subject>Electric power generation</subject><subject>Electric vehicles</subject><subject>Forecasting</subject><subject>Fractals</subject><subject>Load modeling</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Predictive models</subject><subject>Regression analysis</subject><subject>Self-similarity</subject><subject>System-on-chip</subject><subject>Time series</subject><subject>Time series analysis</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kM1LAzEQxRdRsGjvgpeAFy9b87H52KNUa4WKQtVryCazmrLdaLJF_O9NqXjowbnMhPm9yeMVxRnBE0JwffW8vJtQTPiEVlwpTg-KEamrumRYkMO_mbPjYpzSCudijAlaj4r5Q3DQ-f4Nmd6hWYhgTRq27yV0bbn0a9-ZiJ7CF0S0CMahmw2gIaDbVzTLJJq-m_gGMZ0WR63pEox_-0nxMrt9ns7LxePd_fR6UVpGq6FsnVC2cUI62ajsQpDWSsCm5oKDaaiwqjbSCSypo1XdWMVUwysJqlKVY4SdFJe7ux8xfG4gDXrtk4WuMz2ETdJEUc4ZJZRn9GIPXYVN7LM7TaSShHMsq0zhHWVjSClCqz-iX5v4rQnW23R1Tldv09W_6WaJ2JNYP5jBh36Ixnf_Cc93Qg8Af__I7Jnl7Q9gIYQ7</recordid><startdate>20160501</startdate><enddate>20160501</enddate><creator>Korolko, Nikita</creator><creator>Sahinoglu, Zafer</creator><creator>Nikovski, Daniel</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes the time series obtained by this simulator and its properties. We show that the power load aggregated over a number of fast chargers (after deseasonalizing and elimination of the linear trend) is a self-similar process with the Hurst parameter 0.57 <; H <; 0.67, where H varies depending on the multiplexing level. The main contribution of this paper is empirical evidence that a fitted fractional autoregressive integrated moving average (fARIMA) model taking into account self-similarity of the load time series can yield high-quality short-term forecasts when H is large enough. Namely, the fitted fARIMA model uniformly outperforms regular ARIMA algorithms in terms of root-mean-square error for predictions with time horizon up to 120 min for H ≥ 0.639. Moreover, we show that the fARIMA advantage on average grows as a function of the Hurst exponent H. Computational experiments demonstrate that this edge is stably greater than 1.1% and can be as high as 5-7% for some scenarios.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TSG.2015.2458852</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Aggregates Automotive components Computer simulation Demand forecasting Electric power generation Electric vehicles Forecasting Fractals Load modeling Mathematical models Modelling Predictive models Regression analysis Self-similarity System-on-chip Time series Time series analysis |
| title | Modeling and Forecasting Self-Similar Power Load Due to EV Fast Chargers |
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