A model-based smart meters time series decomposition approach for demand flexibility characterization of SMEs and households

Demand flexibility is a fundamental service in the current energy system transformation contributing to balance intermittent renewable generation with increasing electrified demand. This paper proposes an innovative model-based methodology for smart meters time series decomposition to characterize and segment customers for demand flexibility programs. First, the daily energy times are split into normalized daily and hourly profiles, and periods of structurally low energy use are identified. The series is then decomposed using a dynamic regression and ARIMA-GARCH approach, resulting in residuals that follow a non-Gaussian white noise distribution. Next, complexity reduction for normalized hourly energy use is achieved via a regression-based feature engineering model, which feeds into a k-means clustering procedure to determine similar hourly energy profile patterns for working week and weekend days. The decomposition yields a cohesive set of data-driven, baseline-free, and explainable metrics that quantify and characterize the demand flexibility potential of each customer and cluster. These metrics capture key dimensions such as flexibility quantity, variability, reversion speed, calendar effects, and predictability. The methodology is validated on a real publicly available dataset of Irish households and SMEs customers. The results highlight the robustness and replicability of the approach while providing actionable insights and comparison between the two customer segments. This approach enables energy companies, engaged citizens and other stakeholders to design and deploy effective demand flexibility strategies in the energy industry.