[HTML][HTML] An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are
challenged by unpredicted changes in operational environments due to climate change and …
challenged by unpredicted changes in operational environments due to climate change and …
Review on photovoltaic with battery energy storage system for power supply to buildings: Challenges and opportunities
B Li, Z Liu, Y Wu, P Wang, R Liu, L Zhang - Journal of Energy Storage, 2023 - Elsevier
Photovoltaic (PV) has been extensively applied in buildings, adding a battery to building
attached photovoltaic (BAPV) system can compensate for the fluctuating and unpredictable …
attached photovoltaic (BAPV) system can compensate for the fluctuating and unpredictable …
[HTML][HTML] Transfer learning in demand response: A review of algorithms for data-efficient modelling and control
A number of decarbonization scenarios for the energy sector are built on simultaneous
electrification of energy demand, and decarbonization of electricity generation through …
electrification of energy demand, and decarbonization of electricity generation through …
Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency
Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic
(PV) technology and electric battery storage, are increasingly being considered as solutions …
(PV) technology and electric battery storage, are increasingly being considered as solutions …
Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level
Installing the battery energy storage system (BESS) and optimizing its schedule to effectively
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …
[HTML][HTML] An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach
Occupants' behavior is a major source of uncertainty for the optimal operation of building
energy systems. The highly stochastic hot water use behavior of occupants has led to …
energy systems. The highly stochastic hot water use behavior of occupants has led to …
A systematic review of reinforcement learning application in building energy-related occupant behavior simulation
H Yu, VWY Tam, X Xu - Energy and Buildings, 2024 - Elsevier
The building and construction industry has consistently been a major contributor to energy
consumption and carbon emissions. With stochastic interactions between occupants and …
consumption and carbon emissions. With stochastic interactions between occupants and …
Optimal planning of a rooftop PV system using GIS-based reinforcement learning
This study aimed to develop a geographic information system (GIS)-based reinforcement
learning (RL) model for optimal planning of a rooftop PV system, considering the uncertainty …
learning (RL) model for optimal planning of a rooftop PV system, considering the uncertainty …
[HTML][HTML] Transfer learning applied to DRL-Based heat pump control to leverage microgrid energy efficiency
Domestic hot water accounts for approximately 15% of the total residential energy
consumption in Europe, and most of this usage happens during specific periods of the day …
consumption in Europe, and most of this usage happens during specific periods of the day …
[HTML][HTML] The reinforcement learning method for occupant behavior in building control: A review
Occupant behavior in buildings has been considered the major source of uncertainty for
assessing energy consumption and building performance. Modeling frameworks are usually …
assessing energy consumption and building performance. Modeling frameworks are usually …