{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T11:07:14Z","timestamp":1777028834995,"version":"3.51.4"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"DIGI-USER research platform of Lappeenranta-Lahti University of Technology LUT, Finland"},{"DOI":"10.13039\/501100005637","name":"Tekniikan Edist\u00e4miss\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Select. Areas Commun."],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1109\/jsac.2019.2952195","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T20:41:46Z","timestamp":1573159306000},"page":"96-109","source":"Crossref","is-referenced-by-count":25,"title":["Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7370-3805","authenticated-orcid":false,"given":"Aleksei","family":"Mashlakov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7691-121X","authenticated-orcid":false,"given":"Lasse","family":"Lensu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arto","family":"Kaarna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ville","family":"Tikka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-474X","authenticated-orcid":false,"given":"Samuli","family":"Honkapuro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","volume":"84","author":"mclachlan","year":"1988","journal-title":"Mixture Models Inference and Applications to Clustering"},{"key":"ref38","article-title":"Mixture density networks","author":"bishop","year":"1994"},{"key":"ref33","first-page":"5574","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2749512"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2017.2706563"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2015.02.010"},{"key":"ref37","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv 1409 0473"},{"key":"ref36","article-title":"Antisymmetricrnn: A dynamical system view on recurrent neural networks","author":"chang","year":"2019","journal-title":"arXiv 1902 09689"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2686012"},{"key":"ref34","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref60","article-title":"cuDNN: Efficient primitives for deep learning","author":"chetlur","year":"2014","journal-title":"Arxiv 1410 0759"},{"key":"ref62","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref61","first-page":"57","article-title":"Statsmodels: Econometric and statistical modeling with python","author":"seabold","year":"2010","journal-title":"Proc 9th Python in Science Conf"},{"key":"ref63","author":"ghenis","year":"2019","journal-title":"Quantile Regression From OLS to TensorFlow"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.08.080"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"13","DOI":"10.25080\/Majora-8b375195-003","article-title":"Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms","author":"bergstra","year":"2013","journal-title":"Proc 12th Python Sci Conf"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2013.2273989"},{"key":"ref65","author":"pumperla","year":"2017","journal-title":"Hyperas A Very Simple Convenience Wrapper Around Hyperopt for Fast Prototyping With Keras Models"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2354418"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/EEM.2018.8469998"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate2564"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2741578"},{"key":"ref20","year":"2019","journal-title":"Network frequency"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"7290","DOI":"10.3182\/20140824-6-ZA-1003.02615","article-title":"Impact of low rotational inertia on power system stability and operation","volume":"47","author":"ulbig","year":"2014","journal-title":"IFAC Proc Vols"},{"key":"ref21","year":"2019","journal-title":"Frequency data"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-018-0441-1"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2012.2220867"},{"key":"ref26","article-title":"Frequency characterization and control for future low inertia systems","author":"nguyen","year":"2018"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2426017"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1002\/1099-131X(200007)19:4<299::AID-FOR775>3.3.CO;2-M"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2016.02.001"},{"key":"ref59","author":"martin","year":"2019","journal-title":"Keras Mixture Density Network Layer"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/mnras\/sty3217"},{"key":"ref57","author":"ratsimbazafy","year":"2018","journal-title":"Mckinsey smartcities traffic prediction"},{"key":"ref56","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc 5th USENIX Conf Operating Syst Design Implementation"},{"key":"ref55","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30241-2_41"},{"key":"ref53","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.02.006"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2018.2858183"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2502423"},{"key":"ref40","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2807845"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2437877"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.01.033"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.05.234"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.05.212"},{"key":"ref17","first-page":"1","article-title":"Storage-based frequency control and grid-frequency deviations forecasting","volume":"2016","author":"mercier","year":"2016","journal-title":"Revue E tijdschrift"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/e19100552"},{"key":"ref19","year":"2019","journal-title":"Historic Frequency Data"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2810781"},{"key":"ref3","first-page":"1","article-title":"Electricity storage and renewables: Costs and markets to 2030","author":"ralon","year":"2017","journal-title":"International Renewable Energy Agency"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2870041"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2013.2274465"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.1396"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.11.011"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16773-7_6"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2858265"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.2307\/1913643"},{"key":"ref45","author":"mashlakov","year":"2019","journal-title":"BESS SOC forecasting"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"ref47","first-page":"983","article-title":"Quantile regression forests","volume":"7","author":"meinshausen","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref42","first-page":"1019","article-title":"A theoretically grounded application of dropout in recurrent neural networks","author":"gal","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/EEM.2019.8916335"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.3390\/en10122107","article-title":"Lithium-ion battery storage for the grid&#x2014;A review of stationary battery storage system design tailored for applications in modern power grids","volume":"10","author":"hesse","year":"2017","journal-title":"Energies"}],"container-title":["IEEE Journal on Selected Areas in Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/49\/8974630\/08894041.pdf?arnumber=8894041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T14:24:04Z","timestamp":1651069444000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8894041\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":66,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jsac.2019.2952195","relation":{},"ISSN":["0733-8716","1558-0008"],"issn-type":[{"value":"0733-8716","type":"print"},{"value":"1558-0008","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1]]}}}