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

• Household ToU demand response is significant but remains moderate in scale. • Individual models show strong heterogeneity and counterintuitive reactions. • Smart meter feature extraction enables predictive models of flexibility. • Explainable AI highlights variance and predictability as key flexibility drivers. • Results support AI platforms to automate and scale demand flexibility. 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.