# One for Many: Transfer Learning for Building HVAC Control

arXiv: https://arxiv.org/abs/2008.03625 · HTML: https://mlsanity.com/paper/2008.03625/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization](https://mlsanity.com/paper/2106.06916/) (2021)
- [Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems](https://mlsanity.com/paper/2306.13867/) (2023)
- [Efficient Model Editing with Task-Localized Sparse Fine-tuning](https://mlsanity.com/paper/2504.02620/) (2025)
- [POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems](https://mlsanity.com/paper/2304.01218/) (2023)
- [Joint Differentiable Optimization and Verification for Certified Reinforcement Learning](https://mlsanity.com/paper/2201.12243/) (2022)
- [Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments](https://mlsanity.com/paper/2103.01886/) (2021)
- [Variational Nested Dropout](https://mlsanity.com/paper/2101.11353/) (2021)
- [Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning](https://mlsanity.com/paper/2106.14144/) (2021)
- [Active Reinforcement Learning for Robust Building Control](https://mlsanity.com/paper/2312.10289/) (2023)
- [An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control](https://mlsanity.com/paper/2401.05737/) (2024)
- [Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning](https://mlsanity.com/paper/2503.19212/) (2025)
- [Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning](https://mlsanity.com/paper/2607.16534/) (2026)
- [Co-designing Intelligent Control of Building HVACs and Microgrids](https://mlsanity.com/paper/2107.08378/) (2021)
- [A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings](https://mlsanity.com/paper/2305.00365/) (2023)
- [BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2507.07769/) (2025)
- [RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting](https://mlsanity.com/paper/2606.02852/) (2026)

## Abstract (arXiv)

The design of building heating, ventilation, and air conditioning (HVAC) system is critically important, as it accounts for around half of building energy consumption and directly affects occupant comfort, productivity, and health. Traditional HVAC control methods are typically based on creating explicit physical models for building thermal dynamics, which often require significant effort to develop and are difficult to achieve sufficient accuracy and efficiency for runtime building control and scalability for field implementations. Recently, deep reinforcement learning (DRL) has emerged as a promising data-driven method that provides good control performance without analyzing physical models at runtime. However, a major challenge to DRL (and many other data-driven learning methods) is the long training time it takes to reach the desired performance. In this work, we present a novel transfer learning based approach to overcome this challenge. Our approach can effectively transfer a DRL-based HVAC controller trained for the source building to a controller for the target building with minimal effort and improved performance, by decomposing the design of neural network controller into a transferable front-end network that captures building-agnostic behavior and a back-end network that can be efficiently trained for each specific building. We conducted experiments on a variety of transfer scenarios between buildings with different sizes, numbers of thermal zones, materials and layouts, air conditioner types, and ambient weather conditions. The experimental results demonstrated the effectiveness of our approach in significantly reducing the training time, energy cost, and temperature violations.