arXiv Machine Learning July 15, 2026 By Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston Jr, Mark Goldsworthy, Lachlan O'Neil

CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data

Read the original on 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 12

Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

arXiv:2512. 22287v3 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier.

By Zikun Guo, Adeyinka. P. Adedigba, Rammohan Mallipeddi

diffusion safety

arXiv AI

Aug 5

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

arXiv:2608. 03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment.

By Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang

diffusion safety

Hugging Face Trending Papers

Aug 4

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation.

diffusion safety

arXiv AI

Jul 14

WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs

arXiv:2607. 10720v1 Announce Type: new Abstract: The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids.

By Mohannad Takrouri, Nicolas M. Cuadrado A., Martin Tak\'a\v{c}

llms agents benchmarks safety

arXiv AI

Jun 2

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.

By Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang

benchmarks

arXiv AI

Aug 3

MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation

arXiv:2607. 29177v1 Announce Type: cross Abstract: Utility data (e.

By Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang

diffusion benchmarks

Related stories

Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation