arXiv Machine Learning

Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control

The paper introduces the Estimator from Scratch, a neural network that embeds RC thermal building equations to estimate parameters, and its extension, the Pretrained Estimator, which is pretrained on multiple buildings to avoid initial‑guess dependence. Both methods outperform a genetic‑algorithm estimator and a fully black‑box neural network across simulated and real buildings, achieving superior prediction accuracy and lower, more consistent MPC costs. The Pretrained Estimator is highlighted as a robust, computationally efficient, and initial‑guess‑free alternative for RC parameter estimation, with potential applicability to other control‑oriented dynamical systems.

arXiv Machine Learning

Sep 14

Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models

The paper presents a zero‑shot model predictive control (MPC) approach for buildings that uses excitation‑based generalized transfer learning models. By pretraining on purposefully probed operational data from multiple source buildings, the authors demonstrate that these models achieve superior control performance in 32 simulated target buildings, outperforming both an online linear MPC and a PI controller. This method eliminates the need for target‑specific data, reducing setup cost and facilitating broader deployment of energy‑efficient MPC in the building sector.

By Fabian Raisch, Felix Koch, Zack Xuereb Conti, Christoph Goebel, Benjamin Tischler

fine-tuning benchmarks

arXiv Machine Learning

Jul 17

GenTL: A General Transfer Learning Model for Building Thermal Dynamics

arXiv:2501. 13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics.

By Fabian Raisch, Thomas Krug, Christoph Goebel, Benjamin Tischler

fine-tuning

Hugging Face Trending Papers

Aug 20

ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control

Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critical role for urban climate mitigation aligned with UN Sustainable Development Goals 11 and 13.

diffusion reinforcement-learning benchmarks safety

arXiv AI

Jun 30

Agile Reinforcement Learning through Separable Neural Architecture and Applications

arXiv:2601. 23225v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value functions.

By Rajib Mostakim, Reza T. Batley, Sourav Saha

reinforcement-learning efficiency benchmarks safety

arXiv AI

Aug 21

ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control

arXiv:2608. 19804v1 Announce Type: new Abstract: Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions.

By Xu Yang, Kailai Sun, Dianyu Zhong, Qianchuan Zhao

diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 10

Generalizing HVAC Control With Domain Randomized Reinforcement Learning

The paper introduces NOMAD‑RL, a reinforcement learning controller for HVAC systems that learns to adapt across diverse thermal zones via a universal thermostat interface. It employs an adaptive domain randomization scheme using physics‑informed normalizing flows to generate realistic, multimodal training data, enabling the recurrent policy to handle partial observability. Experiments show NOMAD‑RL outperforms constant‑setpoint PID and non‑randomized RL, and rivals well‑tuned model predictive control, especially in multi‑zone scenarios.

By Pablo Boitel, Kun Zhang

reinforcement-learning multimodal

arXiv Machine Learning

Jul 20

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC

arXiv:2510. 01475v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC), but deploying MPC often requires substantial engineering effort.

By Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es

reinforcement-learning

arXiv AI

Aug 11

Tools to Explain Neural Networks for Power System Dynamics

arXiv:2608. 08048v1 Announce Type: cross Abstract: This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics.

By Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis

arXiv AI

Jul 14

Constrained Reinforcement Learning for Safe Heat Pump Control

arXiv:2409. 19716v2 Announce Type: replace-cross Abstract: Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks.

By Baohe Zhang, Lilli Frison, Thomas Brox, Joschka B\"odecker

reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 11

Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints

arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.

By Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg

benchmarks

arXiv Machine Learning

Jun 3

RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.

By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri

llms robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 14

Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations

arXiv:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.

By Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-S{\o}rensen

llms

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