{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T01:17:47Z","timestamp":1778548667975,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T00:00:00Z","timestamp":1577318400000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000120818\/17\/NL\/US"],"award-info":[{"award-number":["4000120818\/17\/NL\/US"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.<\/jats:p>","DOI":"10.1186\/s42162-019-0102-2","type":"journal-article","created":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T17:02:34Z","timestamp":1577379754000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Learning capacity: predicting user decisions for vehicle-to-grid services"],"prefix":"10.1186","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6999-8487","authenticated-orcid":false,"given":"Rob","family":"Shipman","sequence":"first","affiliation":[]},{"given":"Sophie","family":"Naylor","sequence":"additional","affiliation":[]},{"given":"James","family":"Pinchin","sequence":"additional","affiliation":[]},{"given":"Rebecca","family":"Gough","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Gillott","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,26]]},"reference":[{"key":"102_CR1","unstructured":"Abadi M, Agarwal A, Barham P, et al (2015) TensorFlow: large-scale machine learning on heterogeneous distributed systems"},{"key":"102_CR2","doi-asserted-by":"publisher","unstructured":"Andersen PB, Sousa T, Thingvad A, et al (2018) Added value of individual flexibility profiles of electric vehicle users for ancillary services. 2018 IEEE Int Conf Commun Control Comput Technol Smart Grids, SmartGridComm 2018 1\u20136. doi: https:\/\/doi.org\/10.1109\/SmartGridComm.2018.8587585","DOI":"10.1109\/SmartGridComm.2018.8587585"},{"key":"102_CR3","unstructured":"Bates J, Leibling D (2012) Spaced out perspectives on parking policy"},{"key":"102_CR4","first-page":"177","volume-title":"Proceedings of COMPSTAT\u20192010","author":"L Bottou","year":"2010","unstructured":"Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Lechevallier Y, Saporta G (eds) Proceedings of COMPSTAT\u20192010. Physica-Verlag HD, Heidelberg, pp 177\u2013186"},{"key":"102_CR5","unstructured":"Butcher L, Edmonds T (2018) Automated and electric vehicles act 2018 briefing paper"},{"key":"102_CR6","doi-asserted-by":"crossref","unstructured":"Cross JD, Hartshorn R (2016) My electric avenue: integrating electric vehicles into the electrical networks. In: 6th Hybrid and electric vehicles conference (HEVC 2016). Institution of Engineering and Technology, pp 12 (6 .)\u201312 (6 )","DOI":"10.1049\/cp.2016.0972"},{"key":"102_CR7","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.enpol.2018.05.004","volume":"120","author":"J Geske","year":"2018","unstructured":"Geske J, Schumann D (2018) Willing to participate in vehicle-to-grid (V2G)? Why not! Energy Policy 120:392\u2013401. https:\/\/doi.org\/10.1016\/j.enpol.2018.05.004","journal-title":"Energy Policy"},{"key":"102_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.apenergy.2017.01.102","volume":"192","author":"Rebecca Gough","year":"2017","unstructured":"Gough R, Dickerson C, Rowley P, Walsh C (2017) Vehicle-to-grid feasibility: a techno-economic analysis of EV-based energy storage. Appl Energy:192, 12\u2013123. https:\/\/doi.org\/10.1016\/j.apenergy.2017.01.102","journal-title":"Applied Energy"},{"key":"102_CR9","unstructured":"International Energy Agency (2018a) Renewables 2018 - market analysis and forecast from 2018 to 2023. IEA:1\u20135"},{"key":"102_CR10","doi-asserted-by":"publisher","unstructured":"International Energy Agency (2018b) Global EV Outlook 2018. IEA. https:\/\/doi.org\/10.1787\/9789264302365-en","DOI":"10.1787\/9789264302365-en"},{"key":"102_CR11","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TKDE.2005.86","volume":"17","author":"SY Jung","year":"2005","unstructured":"Jung SY, Hong JH, Kim TS (2005) A statistical model for user preference. IEEE Trans Knowl Data Eng 17:834\u2013842. https:\/\/doi.org\/10.1109\/TKDE.2005.86","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"102_CR12","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.jpowsour.2004.12.022","volume":"144","author":"W Kempton","year":"2005","unstructured":"Kempton W, Tomi\u0107 J (2005) Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J Power Sources 144:280\u2013294. https:\/\/doi.org\/10.1016\/j.jpowsour.2004.12.022","journal-title":"J Power Sources"},{"key":"102_CR13","unstructured":"Liu X (2015) Modeling users\u2019 dynamic preference for personalized recommendation. In: Proceedings of the 24th international conference on artificial intelligence. AAAI press, pp 1785\u20131791"},{"key":"102_CR14","doi-asserted-by":"crossref","unstructured":"Naylor S, Pinchin J, Gough R, Gillott M (2019) Vehicle availability profiling from diverse data sources. In: IEEE International Conference on Pervasive Computing and Communications Workshops. Kyoto","DOI":"10.1109\/PERCOMW.2019.8730716"},{"key":"102_CR15","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.tranpol.2018.08.004","volume":"71","author":"L Noel","year":"2018","unstructured":"Noel L, Zarazua de Rubens G, Kester J, Sovacool BK (2018) Beyond emissions and economics: rethinking the co-benefits of electric vehicles (EVs) and vehicle-to-grid (V2G). Transp Policy 71:130\u2013137. https:\/\/doi.org\/10.1016\/j.tranpol.2018.08.004","journal-title":"Transp Policy"},{"key":"102_CR16","unstructured":"Nord Pool Group (2019) Single hourly order. https:\/\/www.nordpoolgroup.com\/trading\/Day-ahead-trading\/Order-types\/Hourly-bid\/."},{"key":"102_CR17","unstructured":"Nuvve Corporation V2G Technology. https:\/\/nuvve.com\/technology\/. Accessed 13 May 2019"},{"key":"102_CR18","unstructured":"Observations AL, Des LAN, Document T, et al (2014) Road vehicles -- vehicle-to-grid communication interface -- part 2: network and application protocol requirements. Iso 15118-22014 342"},{"key":"102_CR19","unstructured":"Ofgem, BEIS (2017) Upgrading our energy system: smart systems and flexibility plan"},{"key":"102_CR20","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/TII.2011.2158841","volume":"7","author":"P Palensky","year":"2011","unstructured":"Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Informatics 7:381\u2013388. https:\/\/doi.org\/10.1109\/TII.2011.2158841","journal-title":"IEEE Trans Ind Informatics"},{"key":"102_CR21","unstructured":"Payne G (2019) Understanding the true value of V2G"},{"key":"102_CR22","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1049\/iet-rpg:20060023","volume":"1","author":"D Pudjianto","year":"2007","unstructured":"Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. IET Renew Power Generat 1:10. https:\/\/doi.org\/10.1049\/iet-rpg:20060023","journal-title":"IET Renew Power Generat"},{"issue":"2","key":"102_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1689239.1689243","volume":"6","author":"Sasank Reddy","year":"2010","unstructured":"Reddy S, Mun M, Burke J et al (2010) Using mobile phones to determine transportation modes. ACM Trans Sens Networks. https:\/\/doi.org\/10.1145\/1689239.1689243","journal-title":"ACM Transactions on Sensor Networks"},{"key":"102_CR24","unstructured":"Reynolds P, Jones F, Lock B, et al (2018) V2G global roadtrip: around the world in 50 projects"},{"key":"102_CR25","doi-asserted-by":"publisher","first-page":"13001","DOI":"10.1088\/1748-9326\/aa9c6d","volume":"13","author":"BK Sovacool","year":"2018","unstructured":"Sovacool BK, Noel L, Axsen J, Kempton W (2018) The neglected social dimensions to a vehicle-to-grid (V2G) transition: a critical and systematic review. Environ Res Lett 13:13001. https:\/\/doi.org\/10.1088\/1748-9326\/aa9c6d","journal-title":"Environ Res Lett"},{"key":"102_CR26","unstructured":"Trakm8 Limited. Fleet management, telematics insurance, optimisation, automotive & cameras. https:\/\/www.trakm8.com\/. Accessed 13 May 2019"},{"key":"102_CR27","doi-asserted-by":"publisher","unstructured":"Wei K, Huang J, Fu S (2007) A survey of E-commerce recommender systems. Proc - ICSSSM\u201907 2007 International Conference\u00a0on\u00a0Service Systems\u00a0and\u00a0Service Management. https:\/\/doi.org\/10.1109\/ICSSSM.2007.4280214","DOI":"10.1109\/ICSSSM.2007.4280214"},{"key":"102_CR28","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.trc.2016.07.006","volume":"71","author":"Christian Will","year":"2016","unstructured":"Will C, Schuller A (2016) Understanding user acceptance factors of electric vehicle smart charging. Transp Res Part C Emerg Technol 71. https:\/\/doi.org\/10.1016\/j.trc.2016.07.006","journal-title":"Transportation Research Part C: Emerging Technologies"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-019-0102-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s42162-019-0102-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-019-0102-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T00:05:38Z","timestamp":1608854738000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-019-0102-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["102"],"URL":"https:\/\/doi.org\/10.1186\/s42162-019-0102-2","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"13 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37"}}