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dc.contributor.authorBucarey, Víctor
dc.contributor.authorLabbé, Martine
dc.contributor.authorMorales-González, Juan Miguel 
dc.contributor.authorPineda-Morente, Salvador 
dc.date.accessioned2022-04-29T07:24:28Z
dc.date.available2022-04-29T07:24:28Z
dc.date.created2022
dc.date.issued2021
dc.identifier.citationVíctor Bucarey, Martine Labbé, Juan M. Morales, Salvador Pineda, An exact dynamic programming approach to segmented isotonic regression, Omega, Volume 105, 2021, 102516, ISSN 0305-0483, https://doi.org/10.1016/j.omega.2021.102516es_ES
dc.identifier.urihttps://hdl.handle.net/10630/24006
dc.description.abstractThis paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can be composed of and on the minimum step length. Our algorithm relies on dynamic programming and is built on the basis that said curve-fitting task can be tackled as a shortest-path type of problem. Numerical results on synthetic and realistic data sets reveal that our algorithm is able to provide the globally optimal monotone stepwise curve fit for samples with thousands of data points in less than a few hours. Furthermore, the algorithm gives a certificate on the optimality gap of any incumbent solution it generates. From a practical standpoint, this piece of research is motivated by the roll-out of smart grids and the increasing role played by the small flexible consumption of electricity in the large-scale integration of renewable energy sources into current power systems. Within this context, our algorithm constitutes an useful tool to generate bidding curves for a pool of small flexible consumers to partake in wholesale electricity markets.es_ES
dc.description.sponsorshipThis research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 755705). This work was also supported in part by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF) through project ENE2017-83775-P. Martine Labbé has been partially supported by the Fonds de la Recherche Scientifique - FNRS under Grant(s) no PDR T0098.18.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAnálisis de regresiónes_ES
dc.subject.otherCardinality-constrained shortest path problemes_ES
dc.subject.otherIsotonic regressiones_ES
dc.subject.otherSegmented regressiones_ES
dc.subject.otherConsumers’ price-responsees_ES
dc.subject.otherInverse optimizationes_ES
dc.subject.otherData clusteringes_ES
dc.titleAn exact dynamic programming approach to segmented isotonic regressiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.identifier.doihttps://doi.org/10.1016/j.omega.2021.102516
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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