Building Energy and Comfort Optimization

Últimos artículos

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

  Fabian Raisch, Felix Koch, Zack Xuereb Conti, Christoph Goebel, Benjamin Tischler · 14 de septiembre de 2026

  The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL) has consequently gained increasing attention for target building modeling, as …

• Designing for Healthy, Affordable, and Sustainable Human-HVAC Interactions for Heating in Smart Homes

  Delong Korus-Du · 9 de septiembre de 2026

  As geopolitical tensions, energy crises, and energy-intensive AI infrastructure intensify concerns about demand, affordability, and resilience, communities increasingly encounter these challenges through everyday energy practices, particularly winter heating. Against this background, the doctoral ex…

• Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

  Alexander Neubauer, Tianzhen Hong, Han Li, Mengbo Yu, Amin Darbandi, Yannick F\"urst, Martin Kriegel · 7 de septiembre de 2026

  Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point naming, missing metadata, and fragmented documentation obstruct their operational use. This systematic review analyses and codes 66 peer-reviewed studies on large language models (LLMs) for HVAC…

• Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings

  Tran Le Vu · 13 de agosto de 2026

  This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical, water-cooled chiller plant and its associated air side. Rather than converging to a single scalarised policy, the controller maintains a product arch…

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

  Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg · 11 de agosto de 2026

  Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available. This study quantifies the trade-off between predictive accuracy and input accessibility using two nationally representative U.S. reside…

• A Physics-Informed Neural Operator for Thermal Ranking of Low-Cost Wall Materials in Hot-Dry Climates

  Muhammad Akbar Khan, Fahim Raees, Ubaida Fatima · 29 de julio de 2026

  Identifying cost-effective indigenous building materials that minimise heat penetration through walls is critical for indoor thermal comfort in low-income rural housing in hot-dry climates, where summer temperatures routinely exceed 45 C. We present a two-stage computational framework for thermal ra…

• Unfit for stranding assessment: a panel-scale multimodal-LLM audit of building-decarbonisation disclosure (BeDA)

  Jingyi Xu, Minghui Cheng, Anchen Sun · 27 de julio de 2026

  Buildings account for roughly 34% of global final energy use and 37% of energy- and process-related CO$_2$ emissions. Stranding regulation now being enacted (New York City Local Law 97, the EU Energy Performance of Buildings Directive recast) presupposes that a building portfolio's carbon intensity …

• Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning

  Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, Pierre-Luc Bacon · 21 de julio de 2026

  Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a critical limitation for real-world deployment. Existing benchmarks offer limited diversity and complexity, making it difficul…

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

  Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es · 20 de julio de 2026

  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. Reinforcement Learning (RL) may offer comparable perfo…

• GenTL: A General Transfer Learning Model for Building Thermal Dynamics

  Fabian Raisch, Thomas Krug, Christoph Goebel, Benjamin Tischler · 17 de julio de 2026

  Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Consequently, it enables the creation of data-efficient models that can be used for ad…

• Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

  Takumi Shioda, Kohei Terashima, Tatsuo Nagai · 15 de julio de 2026

  Buildings are expected to shift cooling loads in response to grid conditions. Thermal energy storage (TES) enables this shift, but scheduling it well requires planning hours ahead under storage constraints. Model predictive control (MPC) and reinforcement learning are difficult to scale across build…

• An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

  Dihia Falouz, Aida Douaibia, Amine Bechar, Youssef Elmir, Abbes Amira, Adel Oulefki · 30 de junio de 2026

  Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use. This paper proposes an end-to-end agentic pipeline that combines deep time-series forecasting, variational anomaly detection, and LLM-based reasoning to generate prioritized…

• ThermoLLM: Thermodynamics-Aware HVAC Control with Spatial-Semantic Knowledge Graph

  Kirtan Bhatt, Xiachong Lin, Matthew Amos, Flora D. Salim, Wen Hu · 23 de junio de 2026

  Multi-zone HVAC control is a spatial decision problem in which indoor thermal evolution and control decisions depend not only on outdoor conditions and internal heat gains but also on zone layout, physical adjacency, and delayed thermal interactions across the building. Recent LLM-based HVAC control…

• Prototyping an AI-powered Tool for Energy Efficiency in New Zealand Homes

  Abdollah Baghaei Daemei · 16 de junio de 2026

  Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions. In New Zealand, housing quality has historically been poor, with inadequate insulation and inefficient heating contributing to widespread energy hardship. Recent reforms, including the Warmer Kiwi Ho…

• Quantifying the Energy Floor: Direct Measurement and Replay Buffer Bias in SAC-Based HVAC Control on sbsim

  Bo Li, Chen Zhang · 2 de junio de 2026

  We quantify the energy floor -- the minimum achievable cost given action space constraints -- for Soft Actor-Critic (SAC) HVAC control on the sbsim calibrated building simulator. Through minimum-action experiments, we directly measure this floor at USD 35.51/day, dominated by continuous electrical l…

• Application of Algorithms in Energy-Efficient Design Platforms for Green Building

  Na Yu, Fu Wenli, Guo Fei · 2 de junio de 2026

  During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization. This paper presents a platform that combines Building Information Modeling (BIM), sensor operational data, and advanced simulation workflows using robust algorithms. …

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

  Shadmehr Zaregarizi, Khashayar Yavari · 29 de mayo de 2026

  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-building energy forecasting based on the Temporal…

• PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management

  Shadmehr Zaregarizi, Khashayar Yavari · 28 de mayo de 2026

  Occupant comfort and grid-aware energy efficiency are competing objectives whose joint optimization depends critically on how reward functions are specified in deep reinforcement learning (DRL) controllers for buildings. Yet reward design remains largely ad hoc: comfort terms are either hand-tuned h…

• OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings

  Shadmehr Zaregarizi, Khashayar Yavari · 28 de mayo de 2026

  Large language models (LLMs) have demonstrated promising capability in generating reward functions for deep reinforcement learning (DRL)-based building energy management. However, their potential to exhibit or exacerbate disparities in occupant comfort across heterogeneous demographic populations re…

• A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation

  Erfan Haghighat Damavandi, Davide Papurello, Mahdi Alibeigi, Armin Keshavarz, Simone Canevarolo, Marco Condo · 26 de mayo de 2026

  Optimizing HVAC (Heating, Ventilation and Air Conditioning) can enhance a building's energy efficiency while providing comfort levels for its occupants. Using conventional control systems to maintain HVAC functions is often difficult because of the nonlinear characteristics of a building envelope as…

• Gated Multimodal Learning for Interpretable Property Energy Performance Prediction and Retrofit Scenario Analysis

  Yunfei Bai, Aaron Tesfa Tsion, Raul Rosales, Barbara Shollock, Wei He · 7 de mayo de 2026

  Achieving resilient and sustainable cities requires scalable approaches to decarbonising residential buildings, which account for about 20% of UK greenhouse gas emissions and 25% of energy-related emissions in the European Union. Energy Performance Certificates (EPCs) support regulation and retrofit…

• Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models

  Jan Marco Ruiz de Vargas, Fabian Raisch, Zoltan Nagy, Pierre Pinson, Christoph Goebel · 7 de mayo de 2026

  Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of predictive modeling and reinforcement learning. While previous MBRL methods applied to HVAC control have reduced training data requirements, they still …

• Toward a foundational thermal model for residential buildings

  Ting-Yu Dai, Kingsley Nweye, Dev Niyogi, Zoltan Nagy · 5 de mayo de 2026

  The building energy community lacks a foundational thermal model, i.e., a single pretrained model capable of generalizing across diverse buildings, climates, and control strategies without building-specific calibration. Achieving this vision requires architectural principles that capture universal t…

• Catalyzing Informed Residential Energy Retrofit Decisions via Domain-Specific LLM

  Lei Shu, Dong Zhao, Jianli Chen, Armin Yeganeh, Sinem Mollaoglu, Jiayu Zhou · 23 de abril de 2026

  Residential energy retrofit initiation is often stalled by an expertise gap, where homeowners lack the technical literacy required for structured building energy assessments and are thereby trapped in low-information environments with fragmented sources. To bridge this gap, this study reports a doma…

• Thermal-GEMs: Generalized Models for Building Thermal Dynamics

  Felix Koch, Fabian Raisch, Benjamin Tischler · 21 de abril de 2026

  Data-driven models for building thermal dynamics are a scalable approach for enabling energy-efficient operation through fault detection & diagnosis or advanced control. To obtain accurate models, measurement data from a target building spanning months to years are required. Transfer Learning (TL) m…