Systematic review on deep reinforcement learning-based energy management for different building types
A Shaqour, A Hagishima - Energies, 2022 - mdpi.com
Owing to the high energy demand of buildings, which accounted for 36% of the global share
in 2020, they are one of the core targets for energy-efficiency research and regulations …
in 2020, they are one of the core targets for energy-efficiency research and regulations …
[HTML][HTML] Breaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence
Advances in tunnel boring machines (TBM) have leveraged applied artificial intelligence to
promote sustainable and automatic tunneling construction. This paper highlights the …
promote sustainable and automatic tunneling construction. This paper highlights the …
MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities
Building and power generation decarbonization present new challenges in electric grid
reliability as a result of renewable energy source intermittency and increase in grid load …
reliability as a result of renewable energy source intermittency and increase in grid load …
[HTML][HTML] Predictive control and coordination for energy community flexibility with electric vehicles, heat pumps and thermal energy storage
C Srithapon, D Månsson - Applied Energy, 2023 - Elsevier
Electrification of private transportation and residential heating is a potential action to
decrease significantly carbon emissions. However, the lack of coordination of such …
decrease significantly carbon emissions. However, the lack of coordination of such …
Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings
Abstract In commercial buildings, Heat, Ventilation, and Air Conditioning (HVAC) systems
account for about 40–50% of total electricity usage, contributing to an economic burden on …
account for about 40–50% of total electricity usage, contributing to an economic burden on …
CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
As more distributed energy resources become part of the demand-side infrastructure,
quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an …
quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an …
Powergridworld: A framework for multi-agent reinforcement learning in power systems
We present the PowerGridworld open source software package to provide users with a
lightweight, modular, and customizable framework for creating power-systems-focused, multi …
lightweight, modular, and customizable framework for creating power-systems-focused, multi …
[HTML][HTML] RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks
Abstract Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing
Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL …
Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL …
Price perturbations for privacy preserving demand response with distribution network awareness
Demand response (DR), where electricity consumption is shifted in response to incentive
signals, can ease the transition to renewable generation. However, when many devices …
signals, can ease the transition to renewable generation. However, when many devices …
Powergym: A reinforcement learning environment for volt-var control in power distribution systems
Reinforcement learning for power distribution systems has so far been studied using
customized environments due to the proprietary nature of the power industry. To encourage …
customized environments due to the proprietary nature of the power industry. To encourage …