2021
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Scarcello, L.; Spezzano, G.; Vinci, A.
Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning Proceedings Article
In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), IEEE, 2021, ISBN: 9781665401708.
Abstract | Links | BibTeX | Tag: Cognitive Buildings, Deep Reinforcement Learning, smart environments, Thermal Comfort
@inproceedings{Cicirelli2021,
title = {Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and L. Scarcello and G. Spezzano and A. Vinci},
doi = {10.1109/ICHMS53169.2021.9582638},
isbn = {9781665401708},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)},
journal = {Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021},
publisher = {IEEE},
abstract = {The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.},
keywords = {Cognitive Buildings, Deep Reinforcement Learning, smart environments, Thermal Comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.
2020
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Spezzano, G.; Vinci, A.
Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop Proceedings Article
In: 2020 IEEE International Conference on Human-Machine Systems (ICHMS), IEEE, 2020, ISBN: 9781728158716.
Abstract | Links | BibTeX | Tag: Cognitive Building., Deep Reinforcement Learning, smart environments, Thermal Comfort
@inproceedings{Cicirelli2020,
title = {Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and G. Spezzano and A. Vinci},
doi = {10.1109/ICHMS49158.2020.9209555},
isbn = {9781728158716},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE International Conference on Human-Machine Systems (ICHMS)},
journal = {Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020},
publisher = {IEEE},
abstract = {The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people's comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.},
keywords = {Cognitive Building., Deep Reinforcement Learning, smart environments, Thermal Comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people's comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.
2019
Cicirelli, F.; Guerrieri, A.; Mastroianni, C.; Palopoli, F.; Spezzano, G.; Vinci, A.
Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning Proceedings Article
In: 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, 2019, ISBN: 9781728129235.
Abstract | Links | BibTeX | Tag: Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings
@inproceedings{Cicirelli2019,
title = {Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning},
author = {F. Cicirelli and A. Guerrieri and C. Mastroianni and F. Palopoli and G. Spezzano and A. Vinci},
doi = {10.1109/DS-RT47707.2019.8958661},
isbn = {9781728129235},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
journal = {Proceedings - 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019},
publisher = {IEEE},
abstract = {This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.},
keywords = {Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.