mlsanityData to 14 Oct 2026

The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability

arXiv 2410.03756 ↗First posted 2 Oct 2024cs.AI cs.CY cs.DCcited by 5

Not on the map. Why?

Abstract (arXiv)

Commercial buildings account for 17% of U.S. carbon emissions, with roughly half of that from Heating, Ventilation, and Air Conditioning (HVAC). HVAC devices form a complex thermodynamic system, and while Model Predictive Control and Reinforcement Learning have been used to optimize control policies, scaling to thousands of buildings remains a significant unsolved challenge. Most current algorithms are over-optimized for specific buildings and rely on proprietary data or hard-to-configure simulations. We present the Smart Buildings Control Suite, the first open source interactive HVAC control benchmark with a focus on solutions that scale. It consists of 3 components: real-world telemetric data extracted from 11 buildings over 6 years, a lightweight data-driven simulator for each building, and a modular Physically Informed Neural Network (PINN) building model as a simulator alternative. The buildings span a variety of climates, management systems, and sizes, and both the simulator and PINN easily scale to new buildings, ensuring solutions using this benchmark are robust to these factors and only reliant on fully scalable building models. This represents a major step towards scaling HVAC optimization from the lab to buildings everywhere. To facilitate use, our benchmark is compatible with the Gym standard, and our data is part of TensorFlow Datasets.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 98 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

17 of 98 references.

References · 17

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  4. 2016OpenAI Gymcited by 1,736
  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  6. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
  7. 2021Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modelingcited by 41
  8. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 38
  9. 2020Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamicscited by 32
  10. 2021Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimizationcited by 31
  11. 2020Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildingscited by 26
  12. 2021Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildingscited by 24
5 more
  1. 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 21
  2. 2019Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximationcited by 16
  3. 2023A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildingscited by 5
  4. 2023A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Controlcited by 4
  5. 2021Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Buildingcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2026Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learningcited by 1
  2. 2025UrbanAI 2025 Challenge: Linear vs Transformer Models for Long-Horizon Exogenous Temperature Forecastingcited by 0
  3. 2025Time-series Forecast for Indoor Zone Air Temperature with Long Horizons: A Case Study with Sensor-based Data from a Smart Buildingcited by 0
  4. 2026Quantifying the Energy Floor: Direct Measurement and Replay Buffer Bias in SAC-Based HVAC Control on sbsimcited by 0
  5. 2026The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scalecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning
  2. 2026ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control
  3. 2026Scalable Supervisory HVAC Control for Linear Objectives ↗
  4. 2025Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control
  5. 2024Employing Federated Learning for Training Autonomous HVAC Systems
  6. 2026Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
  7. 2024A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
  8. 2026ThermoForce: A Physics-Structured Interventional World Model for Building HVAC Control