Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system
Kai GONG1, Jianlin YANG2(), Xu WANG1(), Chuanwen JIANG1, Zhan XIONG1, Ming ZHANG3, Mingxing GUO3, Ran LV3, Su WANG3, Shenxi ZHANG1
1. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China 2. State Power Investment Corporation Limited Wind Power Innovation Center, Shanghai 201100, China 3. State Grid Shanghai Municipal Electric Power Company, Shanghai 201100, China
Smart buildings have been proven to be a kind of flexible demand response resources in the power system. To maximize the utilization of the demand response resources, such as the heating, ventilating and air-conditioning (HVAC), the energy storage systems (ESSs), the plug-in electric vehicles (PEVs), and the photovoltaic systems (PVs), their controlling, operation and information communication technologies have been widely studied. Involving human behaviors and cyber space, a traditional power system evolves into a cyber-physical-social system (CPSS). Lots of new operation frameworks, controlling methods and potential resources integration techniques will be introduced. Conversely, these new techniques urge the reforming requirement of the techniques on the modeling, structure, and integration techniques of smart buildings. In this paper, a brief comprehensive survey of the modeling, controlling, and operation of smart buildings is provided. Besides, a novel CPSS-based smart building operation structure is proposed, and the integration techniques for the group of smart buildings are discussed. Moreover, available business models for aggregating the smart buildings are discussed. Furthermore, the required advanced technologies for well-developed smart buildings are outlined.
. [J]. Frontiers in Energy, 2022, 16(1): 74-94.
Kai GONG, Jianlin YANG, Xu WANG, Chuanwen JIANG, Zhan XIONG, Ming ZHANG, Mingxing GUO, Ran LV, Su WANG, Shenxi ZHANG. Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system. Front. Energy, 2022, 16(1): 74-94.
A thermal conduction transfer function coupled with finite difference method is developed
The model is implemented in TRNSYS and significantly decreases the achievable time-steps
Ref. [27]
Numerical techniques
A heating and cooling demand estimation model is proposed using the finite element method
The model can help accurately estimate the heating and cooling demand with a different building envelop design
Ref. [28]
Numerical techniques
A multiparameter model order reduction is proposed for thermal modeling
The computational complexity of simulation tools is reduced based on finite elements or finite difference methods
Ref. [4]
Data-driven techniques
A plug-and play learning framework based on IoT technologies is developed, deployed and investigated
The learning framework enables avoidance of building-by-building configuration
Ref. [5]
Data-driven techniques
The IoT technologies and the deep learning approach are utilized to derive accurate thermal model
The experimental results show the proposed fine-grained DNN based model is more accurate than coarse-grained counterpart
Ref. [6]
Data-driven techniques
An intelligent thermal comfort neural network (ITCNN) is proposed for thermal modeling
Preliminary results show that ITCNN outperforms the predicted mean vote based thermal model by an average of 13.1%
Ref. [3]
Hybrid techniques
A “grey-box” based model of heat and cooling energy in smart buildings is proposed
The physical-based formulations of the building envelope and measured data of various building parameters are combined
BIPV modeling
Ref. [31]
PVs
CNN is employed to present the nonlinear relationship between meteorological information and BAPV output
The proposed CNN-based BAPV power forecasting model is superior to the model based on SVR, ANN, and DBN
Ref. [32]
PVs
A dynamic LSTM network is employed for the modeling of rooftop photovoltaic generations
The well-trained LSTM network can duly address the uncertain rooftop PV generations in a rolling-horizon manner
Ref. [33]
PVs with PEVs
A dynamic LSTM network is employed for the modeling of rooftop photovoltaic generations
The well-trained LSTM network can duly address the uncertain rooftop PV generations in a rolling-horizon manner
Ref. [34]
PVs with PEVs
A dynamic LSTM network is employed for the modeling of rooftop photovoltaic generations
The well-trained LSTM network can duly address the uncertain rooftop PV generations in a rolling-horizon manner
Tab.1
Fig.3
Fig.4
Fig.5
VPP
MG
ADN
LA
Location and topology
Regardless of topology constraints
Limited by topology
Limited by topology
Regardless of topology constraints
Market competitiveness
Competitive in day ahead market and ancillary service
Less competitive due to limited reliability
Less competitive due to limited reliability
Competitive in load shifting and load curtailment
Market position
Mainly price-maker
Mainly price-taker
Mainly price-taker
Mainly price-taker
Stakeholders
Distributed generator, demand user
Distributed generator, demand user
Distributed generator, demand user
Demand user
Tab.2
Fig.6
Fig.7
Fig.8
Fig.9
Fig.10
Fig.11
HVAC
Heating, ventilating, and air-conditioning
ESS
Energy storage system
PEV
Plug-in electric vehicle
PV
Photovoltaic system
BIPV
Building integrated photovoltaic system
BHPV
Building attached photovoltaic system
CPSS
Cyber-physical-social system
CPS
Cyber-physical system
VPP
Virtual power plant
CRB
Commercial and residential building
IoT
Internet of things
DNN
Deep neural network
AI
Artificial intelligence
B2G
Building-to-grid
P2P
Peer-to-peer
DR
Demand response
SVR
Support vector regression
DL
Deep learning
RL
Reinforcement learning
CNN
Convolutional neural network
LSTM
Long short-term memory
WPP
Wind power plant
CL
Controlled load
CP
Charging pile
SBA
Smart building aggregator
BEMS
Building energy management system
IAQ
Indoor air quality
DSO
Distributed system operator
MPC
Model predictive control
PDF
Probability density function
MG
Microgrid
ADN
Active distribution network
LA
Load aggregator
ISO
Independent system operator
VESS
Virtual energy storage system
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