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Fecha
2026-01-27
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Elsevier
Citas
Resumen
Unlocking 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.
Descripción
The 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
La 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
La 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
Categorías UNESCO
Palabras clave
Explainable AI, Smart meter data, Demand-side flexibility, Time-of-use pricing, Predictive analytics, Behavioral response
Citación
Bañales, 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
Centro
Escuela Técnica Superior de Ingeniería Informática
Departamento
Informática y Automática
Grupo de investigación
Ingeniería de Sistemas y Control (ISCO)