·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Data center backlash is uniting America1h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7h◆Scoped Semantic Invariance as a Foundation for LLM Reliability7h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems7h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?7h◆How To Build A Modern Diffusion Language Model7h◆Data Optimization for LLMs: A Survey7h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery7h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting7h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents7h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems7h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing7h◆Counterfactual Model Testing Under Distribution Shift7h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security7h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems7h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks7h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches7h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models7h◆Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network7h◆Data center backlash is uniting America1h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7h◆Scoped Semantic Invariance as a Foundation for LLM Reliability7h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems7h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?7h◆How To Build A Modern Diffusion Language Model7h◆Data Optimization for LLMs: A Survey7h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery7h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting7h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents7h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems7h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing7h◆Counterfactual Model Testing Under Distribution Shift7h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security7h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems7h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks7h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches7h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models7h◆Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network7h◆
News/Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
arxiv
PublishedMay 29, 2026 at 4:00 AM
—neutral

Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.29733v1 Announce Type: new Abstract: Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest uncertainty estimates. We present an uncertainty-aware transfer learning (TL) framework for cross-b

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    Temporal Fusion Transformer (TFT)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#energy-forecasting#transfer-learning#uncertainty-estimation#smart-buildings

No replies yet. Be first.

Mentioned models
01
  • 01
    Temporal Fusion Transformer (TFT)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#energy-forecasting#transfer-learning#uncertainty-estimation#smart-buildings

Related coverage

More from ARXIV
arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7harxivScoped Semantic Invariance as a Foundation for LLM Reliability7harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems7h
The Bubble Brief
MON · WED · FRI

Read energy-forecasting insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews