# Machine Learning for Smart and Energy-Efficient Buildings

arXiv: https://arxiv.org/abs/2211.14889 · HTML: https://mlsanity.com/paper/2211.14889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 223 references that are in the map.
- First posted on arXiv 27 Nov 2022.

## Builds on (most cited first, 30 of 37)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks](https://mlsanity.com/paper/1905.04817/) (2019)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [Safe Learning of Quadrotor Dynamics Using Barrier Certificates](https://mlsanity.com/paper/1710.05472/) (2017)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings](https://mlsanity.com/paper/2006.14156/) (2020)
- [A Review of Reinforcement Learning for Autonomous Building Energy Management](https://mlsanity.com/paper/1903.05196/) (2019)
- [Physics guided neural networks for modelling of non-linear dynamics](https://mlsanity.com/paper/2205.06858/) (2022)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [From self-tuning regulators to reinforcement learning and back again](https://mlsanity.com/paper/1906.11392/) (2019)
- [Data-Driven Reachability Analysis with Christoffel Functions](https://mlsanity.com/paper/2104.13902/) (2021)
- [A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks](https://mlsanity.com/paper/1910.02217/) (2019)
- [Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure](https://mlsanity.com/paper/1910.07899/) (2019)
- [Bayesian Safe Learning and Control with Sum-of-Squares Analysis and Polynomial Kernels](https://mlsanity.com/paper/2004.00662/) (2020)
- [Balancing thermal comfort datasets: We GAN, but should we?](https://mlsanity.com/paper/2009.13154/) (2020)
- [Deep Transfer Learning for WiFi Localization](https://mlsanity.com/paper/2103.05123/) (2021)
- [Transfer Learning for Thermal Comfort Prediction in Multiple Cities](https://mlsanity.com/paper/2004.14382/) (2020)
- [Data-driven Predictive Control for Unlocking Building Energy Flexibility: A Review](https://mlsanity.com/paper/2007.14866/) (2020)
- [Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control](https://mlsanity.com/paper/2110.13450/) (2021)
- [Decentralized and Distributed Temperature Control via HVAC Systems in Energy Efficient Buildings](https://mlsanity.com/paper/1702.03308/) (2017)
- [Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response](https://mlsanity.com/paper/2104.14670/) (2021)
- [Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs](https://mlsanity.com/paper/2108.06594/) (2021)
- [Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions](https://mlsanity.com/paper/2112.09995/) (2021)

## Built on by (most cited first, 1 of 1)

- [Heating in human-HVAC interaction for smart homes: An interdisciplinary overview](https://mlsanity.com/paper/2609.23508/) (2026)

## Abstract (arXiv)

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.