Machine Learning for Smart and Energy-Efficient Buildings
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 37 of its 223 references that are in the map. First posted on arXiv 27 Nov 2022.
Builds on
37 of 223 references.
References · 37
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 470
- 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
- 2017Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modelingcited by 130
- 2020House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generationcited by 86
- 2019Physics-Informed Neural Networks for Power Systemscited by 80
- 2019Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networkscited by 76
- 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 35
- 2017Safe Learning of Quadrotor Dynamics Using Barrier Certificatescited by 32
- 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 28
25 more
- 2020Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildingscited by 24
- 2022Physics guided neural networks for modelling of non-linear dynamicscited by 20
- 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 18
- 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
- 2019From self-tuning regulators to reinforcement learning and back againcited by 17
- 2021Data-Driven Reachability Analysis with Christoffel Functionscited by 12
- 2019A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworkscited by 8
- 2019Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructurecited by 8
- 2020Bayesian Safe Learning and Control with Sum-of-Squares Analysis and Polynomial Kernelscited by 7
- 2020Balancing thermal comfort datasets: We GAN, but should we?cited by 7
- 2021Deep Transfer Learning for WiFi Localizationcited by 7
- 2020Transfer Learning for Thermal Comfort Prediction in Multiple Citiescited by 6
- 2020Data-driven Predictive Control for Unlocking Building Energy Flexibility: A Reviewcited by 5
- 2021Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Controlcited by 5
- 2017Decentralized and Distributed Temperature Control via HVAC Systems in Energy Efficient Buildingscited by 3
- 2021Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Responsecited by 3
- 2021Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costscited by 3
- 2021Data-Driven Reachability analysis and Support set Estimation with Christoffel Functionscited by 2
- 2016Data-Driven Evaluation of Building Demand Response Capacitycited by 1
- 2016Optimal Water Heater Control in Smart Home Environmentscited by 1
- 2019HVAC Energy Cost Optimization for a Multi-zone Building via a Decentralized Approachcited by 1
- 2019Stochastic Optimal Control of HVAC system for Energy-efficient Buildingscited by 1
- 2021Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Controlcited by 1
- 2022Conditional Synthetic Data Generation for Personal Thermal Comfort Modelscited by 1
- 2022Asset Participation and Aggregation in Incentive-Based Demand Response Programscited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.