LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics — Paper

Abstract

The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response programs. Understanding these evolving behaviors requires access to granular smart-meter data for applications such as load forecasting, appliance detection, and demand-side flexibility analysis. However, such data are subject to strict access restrictions and data-protection regulations. Thus, realistic synthetic alternatives are necessary. In this paper, we introduce LoaDiff, a diffusion-based generative model for year-long, sub-hourly smart-meter load curves. LoaDiff supports flexible conditioning on static household attributes, such as appliance ownership, and dynamic contextual variables, including calendar information and outdoor temperature. We evaluate the model against multiple generative baselines on three residential electricity-consumption datasets. Our experiments assess four complementary dimensions: fidelity and diversity, training-record memorization risk, downstream utility for load forecasting and appliance detection, and conditional controllability under alternative temperature conditions. The results show that LoaDiff generates realistic and diverse load profiles, achieves a favorable trade-off between generation quality and limited evidence of memorization, preserves information useful for downstream energy applications, and responds coherently to changes in conditioning variables.

Authors 6

Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas

Specification

Official page: arxiv.org/abs/2609.11639
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2609.11639
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.LG, cs.AI, eess.SP
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2609.11639
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

1 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Mariia Baranova, Adrien Petralia, Etienne Le Naour

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Authors

1 claims · 1 propertiesShow all properties

Authorsauthors1

Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperLoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
New paper: LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
arxiv
rss.arxiv.org

Full timeline →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.