DeepAI AI Chat
Log In Sign Up

Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control

by   Vinay Hanumaiah, et al.

It is estimated that about 40 commercial buildings can be attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems. Minimizing the energy cost while considering the thermal comfort of the occupants is very challenging due to unknown and complex relationships between various HVAC controls and thermal dynamics inside a building. To this end, we present a multi-agent, distributed deep reinforcement learning (DRL) framework based on Energy Plus simulation environment for optimizing HVAC in commercial buildings. This framework learns the complex thermal dynamics in the building and takes advantage of the differential effect of cooling and heating systems in the building to reduce energy costs, while maintaining the thermal comfort of the occupants. With adaptive penalty, the RL algorithm can be prioritized for energy savings or maintaining thermal comfort. Using DRL, we achieve more than 75% savings in energy consumption. The distributed DRL framework can be scaled to multiple GPUs and CPUs of heterogeneous types.


page 1

page 2

page 3

page 4


Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning

Heating, Ventilation, and Air Conditioning (HVAC) is extremely energy-co...

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40 is attributed to Heating, Ventilation,...

One for Many: Transfer Learning for Building HVAC Control

The design of building heating, ventilation, and air conditioning (HVAC)...

Multi-agent Deep Reinforcement Learning for Zero Energy Communities

Advances in renewable energy generation and introduction of the governme...

Exploring Deep Reinforcement Learning for Holistic Smart Building Control

In this paper, we take a holistic approach to deal with the tradeoffs be...

End-to-end deep metamodeling to calibrate and optimize energy loads

In this paper, we propose a new end-to-end methodology to optimize the e...