arXiv AI
Calendar-SPCA: Interpretable Representation Learning for Multi-Periodic Electricity Consumption Profiles
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arXiv AI
Sep 17
Calendar-Structured Sparse Principal Component Analysis for Interpretable Multi-Periodic Electricity Consumption Profiles
Calendar-Structured Sparse Principal Component Analysis (Calendar-SPCA) is a new method that learns low-dimensional representations of long-term electricity consumption data by explicitly incorporating daily, weekly, and annual calendar cycles. It uses an L1 penalty and graph total variation to produce sparse, locally coherent, and directly interpretable latent factors. In experiments on the GoiEner and Low Carbon London smart‑meter datasets, Calendar-SPCA retains most of the variance of standard PCA while achieving high sparsity and clear calendar‑aligned structures.
arXiv AI
Jun 15
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv AI
Jun 12
MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv Machine Learning
Jun 3
The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
arXiv:2606. 03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns.
arXiv Machine Learning
Jul 15
CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data
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.
arXiv Machine Learning
Sep 25
Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning
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.