Back to Search
Start Over
DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning
- Source :
- IEEE Internet of Things Journal. 7:8472-8484
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants’ thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants’ thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simu- lation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%. National Research Foundation (NRF) Accepted version This research is supported in part by a project fund from DSAIR@NTU, and a BSEWWT project fund from Na- tional Research Foundation Singapore, administrated through the BSEWWT program office (Ref. BSEWWT2017_2_06), the Green Buildings Innovation Cluster (Grant NO.: NRF2015ENC-GBICRD001-012), administered by Building and Construction Authority (BCA) Singapore.
- Subjects :
- Deep Reinforcement Learning
Computer Networks and Communications
Computer science
020209 energy
02 engineering and technology
Automotive engineering
law.invention
law
Thermal
HVAC
0202 electrical engineering, electronic engineering, information engineering
Reinforcement learning
Smart Building
business.industry
Thermal comfort
Thermal Comfort Prediction
Energy consumption
Computer Science Applications
Hardware and Architecture
Air conditioning
Signal Processing
Ventilation (architecture)
Thermal Comfort Control
Computer science and engineering [Engineering]
Heating, Ventilation, and Air Conditioning
Feedforward neural network
020201 artificial intelligence & image processing
business
Information Systems
Efficient energy use
Subjects
Details
- ISSN :
- 23722541
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Internet of Things Journal
- Accession number :
- edsair.doi.dedup.....26c403589551b70bcf5dd983f4eae9ad