Coincidence-Maximizing Customer Clustering Using Smart Meter Data
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2601. 10494v3 Announce Type: replace-cross Abstract: With grid operators confronting rising uncertainty from renewable integration and a broader push toward electrification, Demand-Side Management (DSM) -- particularly Demand Response (DR) -- has attracted significant attention as a cost-effective mechanism for balancing modern electricity systems.
The paper presents a two-part workflow for analyzing smart‑meter data to uncover patterns of residential co‑adoption of photovoltaic (PV) systems and electric vehicles (EVs). First, dynamic time warping k‑means clustering identifies distinct daily import/export archetypes for PV‑only, EV‑only, co‑adopters, and neither groups, revealing a midday‑centered export pattern for many co‑adopters. Second, a bidirectional LSTM model trained on 21‑day windows achieves high detection performance (AUROC 0.991, macro‑F1 0.906) for PV/EV activity, outperforming tabular baselines and remaining robust across labeling rules and temporal splits.
SmartMeterFM is a conditional flow matching model that, once trained on monthly 15‑minute smart meter data, can generate realistic load and generation profiles, impute missing values, and perform super‑resolution without task‑specific retraining. By conditioning on customer and calendar attributes and using projection‑based guidance during inference, the model preserves temporal patterns and extreme values while matching supplied observations. In large‑scale case studies on Dutch consumption and photovoltaic data, SmartMeterFM achieved lower Continuous Ranked Probability Scores than task‑specific baselines for both imputation and 16‑fold super‑resolution, and matched or outperformed a task‑trained diffusion model on a public residential meter dataset.
The paper presents a method for exact machine unlearning in federated socially fair k‑means clustering, aiming to maintain fairness across demographic groups while allowing data deletion. It introduces separate local summaries per group so that unaffected institutions can keep valid global fairness weights, and demonstrates that reusing cached computations—especially when an entire client withdraws—can dramatically speed up retraining. Experiments on three datasets show significant speedups for initialization and moderate gains for individual record deletions, highlighting when exact unlearning is most beneficial.
Ireland’s smart metering programme records electricity use at 30‑minute intervals, which is too coarse to capture domestic appliance use. The authors present a label‑free disaggregation system that splits usage data into nine appliance categories by combining event detection for high‑power loads with questionnaire‑guided estimation. Evaluated on four datasets—including a large Irish smart‑meter dataset of over 4,800 years of use—the hybrid method achieves the lowest whole‑decomposition error and better month‑level performance than two independently developed systems.
arXiv:2607. 24237v1 Announce Type: new Abstract: Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find similar points.