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Explainable AI for predicting household demand flexibility: Insights from smart meter data and price-based programs

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dc.contributor.authorBañales, Santiago
dc.contributor.authorDormido Canto, Raquel
dc.contributor.authorDuro Carralero, Natividad
dc.date.accessioned2026-03-13T13:28:19Z
dc.date.available2026-03-13T13:28:19Z
dc.date.issued2026-01-27
dc.descriptionThe registered version of this article, first published in “Energy and AI 24 (2026), 100686", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.egyai.2026.100686
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Energy and AI 24 (2026), 100686", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.egyai.2026.100686
dc.description.abstractUnlocking Demand‐Side Flexibility (DSF) at scale is essential for integrating variable renewables and electrified end-uses. We develop a scalable, explainable-AI framework to assess the predictability and drivers of household responsiveness to price-based programs using only data typically available to utilities (smart meters, basic weather, limited socio-economic tags). Using the public Low Carbon London Time-of-Use (ToU) pilot, we first estimate responsiveness with Least Absolute Shrinkage and Selection Operator (LASSO) at both aggregated and household levels—overall and by hour—to quantify effect sizes and heterogeneity. We then train Gradient-Boosting (GB) models and apply SHapley Additive exPlanations (SHAP) to assess the hierarchy and direction of drivers of flexibility. Results show statistically significant but moderate average responses with wide dispersion across households and time-of-day, including a significant percentage of counter-intuitive reactions to price. Features capturing unexplained variability in hourly and daily load (e.g., dispersion measures of residual components) are the strongest positive predictors of flexibility, whereas seasonality/predictability indicators (autocorrelation and seasonal strength) are neutral or negative. SHAP dependence plots reveal clear thresholds, breakpoints, and saturation effects, underscoring the nonlinearity of behavioral response. Because the feature set is derived from routinely collected data, the approach is replicable and operationally practical. The findings enable data-driven targeting of high-potential households and support the design of digital orchestration platforms for near-time demand response, informing tariff design, aggregator strategies, and regulatory guidance for market-based DSF.en
dc.description.provenanceMade available in DSpace on 2026-03-13T13:28:19Z (GMT). No. of bitstreams: 1 DuroCarralero_Natividad_ExplainableAI_NATIVIDAD DURO CARRA.pdf: 10781029 bytes, checksum: fb646241fa05b77b124c97999a70d333 (MD5) Previous issue date: 2026-01-27en
dc.description.versionversión publicada
dc.identifier.citationBañales, Santiago, Dormido, Raquel, Duro, Natividad, (2026). Explainable AI for predicting household demand flexibility: Insights from smart meter data and price-based programs. Energy and AI 24, 100686. https://doi.org/10.1016/j.egyai.2026.100686
dc.identifier.doihttps://doi.org/10.1016/j.egyai.2026.100686
dc.identifier.eissn2666-5468
dc.identifier.urihttps://hdl.handle.net/20.500.14468/32116
dc.journal.titleEnergy and AI
dc.journal.volume24
dc.language.isoen
dc.page.final14
dc.page.initial1
dc.publisherElsevier
dc.relation.centerEscuela Técnica Superior de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.relation.researchgroupIngeniería de Sistemas y Control (ISCO)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203 Ciencia de los ordenadores
dc.subject.keywordsExplainable AIen
dc.subject.keywordsSmart meter dataen
dc.subject.keywordsDemand-side flexibilityen
dc.subject.keywordsTime-of-use pricingen
dc.subject.keywordsPredictive analyticsen
dc.subject.keywordsBehavioral responseen
dc.titleExplainable AI for predicting household demand flexibility: Insights from smart meter data and price-based programsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd8964856-5d49-4779-87df-331494bd4336
relation.isAuthorOfPublicationd5087903-00fc-427e-b4cf-f0592d122b30
relation.isAuthorOfPublication.latestForDiscoveryd8964856-5d49-4779-87df-331494bd4336
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