mlsanityData to 7 Oct 2026

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments

arXiv 2103.01886 ↗First posted 2 Mar 2021cs.LG cs.SY eess.SYcited by 7

Not on the map. Why?

Abstract (arXiv)

This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather data, thus without the need for complex physics-based modelling. We demonstrate the method for joint control of room temperature and bidirectional EV charging to maximize the occupant thermal comfort and energy savings while leaving enough energy in the EV battery for the next trip. We modelled the room temperature with a recurrent neural network and EV charging with a piece-wise linear function. Using these models as a simulation environment, we applied a deep reinforcement learning (DRL) algorithm to obtain an optimal control policy. The learnt policy achieves on average 17% energy savings over the heating season and 19% better comfort satisfaction than a standard RB room temperature controller. When a bidirectional EV is additionally connected and a two-tariff electricity pricing is applied, the MIMO DRL policy successfully leverages the battery and decreases the overall cost of electricity compared to two standard RB controllers, one controlling the room temperature and another controlling the bidirectional EV (dis-)charging. Finally, we demonstrate a successful transfer of the learnt DRL policy from simulation onto a real building, the DFAB HOUSE at Empa Duebendorf in Switzerland, achieving up to 30% energy savings while maintaining similar comfort levels compared to a conventional RB room temperature controller over three weeks during the heating season.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 85 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

14 of 85 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  3. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  4. 2017Recent Trends in Deep Learning Based Natural Language Processingcited by 262
  5. 2015A Critical Review of Recurrent Neural Networks for Sequence Learningcited by 223
  6. 2018Optimal Control Via Neural Networks: A Convex Approachcited by 82
  7. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  8. 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 18
  9. 2020One for Many: Transfer Learning for Building HVAC Controlcited by 16
  10. 2018Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learningcited by 10
  11. 2016Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Drivingcited by 9
  12. 2020Performance-Driven Cascade Controller Tuning with Bayesian Optimizationcited by 7
2 more
  1. 2017Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networkscited by 4
  2. 2021A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing consideredcited by 1

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 0; 2025: 1.

7 papers.

  1. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 16
  2. 2022Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Controlcited by 4
  3. 2022Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Modelscited by 4
  4. 2023Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approachcited by 3
  5. 2022Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learningcited by 1
  6. 2023Path Planning using Reinforcement Learning: A Policy Iteration Approachcited by 0
  7. 2025Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVACcited by 0