[HTML][HTML] Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Abstract Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are a major
source of global operational CO 2 emissions, primarily due to their high energy demands …
source of global operational CO 2 emissions, primarily due to their high energy demands …
Fusion of microgrid control with model-free reinforcement learning: Review and vision
Challenges and opportunities coexist in microgrids as a result of emerging large-scale
distributed energy resources (DERs) and advanced control techniques. In this paper, a …
distributed energy resources (DERs) and advanced control techniques. In this paper, a …
DLMP of competitive markets in active distribution networks: Models, solutions, applications, and visions
Traditionally, the electric distribution system operates with uniform energy prices across all
system nodes. However, as the adoption of distributed energy resources (DERs) propels a …
system nodes. However, as the adoption of distributed energy resources (DERs) propels a …
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] Advancements in data-driven voltage control in active distribution networks: A Comprehensive review
Distribution systems are integrating a growing number of distributed energy resources and
converter-interfaced generators to form active distribution networks (ADNs). Numerous …
converter-interfaced generators to form active distribution networks (ADNs). Numerous …
Interpretable general thermal comfort model based on physiological data from wearable bio sensors: Light Gradient Boosting Machine (LightGBM) and SHapley …
This study aims to develop a general thermal comfort model using physiological signals
obtained from wristband-type wearable biosensors. Accordingly, we constructed and …
obtained from wristband-type wearable biosensors. Accordingly, we constructed and …
Lessons learned from data-driven building control experiments: Contrasting gaussian process-based mpc, bilevel deepc, and deep reinforcement learning
L Di Natale, Y Lian, ET Maddalena… - 2022 IEEE 61st …, 2022 - ieeexplore.ieee.org
This manuscript offers the perspective of experimentalists on a number of modern data-
driven techniques: model predictive control relying on Gaussian processes, adaptive data …
driven techniques: model predictive control relying on Gaussian processes, adaptive data …
A mix-integer programming based deep reinforcement learning framework for optimal dispatch of energy storage system in distribution networks
The optimal dispatch of energy storage systems (ESSs) in distribution networks poses
significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand …
significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand …
Artificial intelligence-based methods for renewable power system operation
Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due
to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and …
to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and …