Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review
Abstract
:1. Introduction
2. Knowledge Graphs
2.1. Definition
2.2. Data Graphs
2.3. Deductive Knowledge
2.4. Inductive Knowledge
2.5. Knowledge Graphs and Digital Twins
3. Methodology
4. Existing Ontologies for Buildings
4.1. Ontologies in Building Design Phase
4.2. Ontologies on Building Operational Phase
- Project Haystack 3 and 4 focus on the representation of buildings entities and concepts utilizing tagsets.
- BASont focuses on building automation and monitoring.
- HTO focuses on streamlining data from IoT based on Project Haystack.
- Brick focuses on metadata and data points from building advancement and needs to be based on end-use applications.
- GDBO represents structured information about buildings and building-installed equipment.
- SBMS is a BAS-protocol-independent model of intelligent building systems, and CTRLont is a model of control logic in BAS.
4.3. Prominent Ontologies for Buildings
4.3.1. Industry Foundation Classes (IFC) Related Ontologies
4.3.2. World Wide Web Consortium (W3C) Related Ontologies and Extensions
4.3.3. Smart Building Related Ontologies
4.3.4. Occupant Behavior Related Ontologies
5. Applications of Ontologies in Buildings
6. Discussion
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
AEC | Architecture, Engineering and Construction |
DSS | Decision Support System |
IoT | Internet of Things |
KG | Knowledge Graph |
UN | United Nations |
SDG | Sustainable Development Goal |
MCDA | Multi-Criteria Decision Analysis |
MOP | Multi-Objective Programming |
LCA | Life Cycle Analysis |
ANN | Artificial Neural Network |
FL | Fuzzy Logic |
EA | Evolutionary Algorithms |
ICT | Information and Communication Technologies |
BIM | Building Information Models |
NBIMS | National Building Information Model Standard |
IFC | Industry Foundation Classes |
gbXML | green building XML |
BAS | Building Automated Systems |
FM | Facility Management |
DT | Digital Twin |
RDF | Resource Description Framework |
IRIs | Internationalized Resource Identifiers |
URIs | Uniform Resource Identifiers |
RDFS | Resource Description Framework Schema |
OWL | Web Ontology Language |
W3C | World Wide Web Consortium |
GNN | Graph Neural Network |
PA | Physical Asset |
WoT | Web of Things |
OneDM | One Data Model |
SEAS | Smart Energy Aware Systems |
BOnSAI | Building Ontology for Ambient Intelligence |
SBOnto | Smart Building Ontology |
SAREF | Smart Applications REFerence |
SBMS | Semantic Building Management System |
HTO | Haystack Tagging Ontology |
GDBO | Google Digital Building Ontology |
REC | Real Estate Core |
BOT | Building Topology Ontology |
BACS | Building Automation and Control Systems |
KM4City | Knowledge Model for City |
FSGIM | Facility Smart Grid Information Model |
DNAs | Drivers Needs Actions & systems |
OP | Occupancy Profile |
BEDES | Building Energy Data Exchange Specification |
VBIS | Virtual Buildings Information System |
OPM | Ontology of Property Management |
LBD | Linked Building Data |
BEO | Building Elements Ontology |
FSO | Flow System Ontology |
BPO | Building Products Ontology |
FOG | Geometry Formats Ontology |
OPM | Ontology for Property Management |
OMG | Ontology for Managing Geometry |
IAQ | Indoor Air Quality |
KPI | Key Performance Indicator |
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Category | Name | Scope/Description | Year | Ref. |
---|---|---|---|---|
Building Design Phase | Industry Foundation Classes (IFC) | Gives spatial and other properties to every building entity | 2013 | [21] |
ifcOWL | Descriptive OWL representation of IFC schema | 2016 | [75] | |
simpleBIM | Simplified version of ifcOWL | 2017 | [116] | |
Green Building XML (gbXML) | Information exchange between BIM and Models | 2000 | [21] | |
Tubes | High-level description of building service systems | 2020 | [77] | |
SimModel Ontology | Exchange of energy simulation data | 2014 | [78] | |
EnergyADE | Exchange of energy simulation data | 2014 | [79] | |
Smart Buildings | Semantic Sensor Network/Sensor, Observation, Sample, and Actuator (SSN/SOSA) | Focuses on sensors in buildings | 2011 | [81] |
Web Thing Model (WoT) | Model to describe the virtual counterpart of physical objects in the Web of Things | 2015 | [82] | |
oneM2M BaseOntology’s | Provide syntactic and semantic interoperability between oneM2M and external systems | 2016 | [83] | |
One Data Model (OneDM) | Model to support a common language for the Internet of Things | 2018 | [84] | |
Smart Energy Aware Systems | 2016 | [85] | ||
ThinkHome | Ontology that includes concepts needed to realize energy efficient and intelligent control mechanisms | 2011 | [86] | |
Building Ontology for Ambient Intelligence (BOnSAI) | A smart building ontology for ambient intelligence | 2012 | [87] | |
DogOnt | Model for all devices being part of IoT inside a smart environment | 2008 | [88] | |
Ontology of Smart Building (SBOnto) | Smart Building Ontology | 2017 | [89] | |
Smart Applications REFerence (SAREF) | Matches existing assets in the smart applications domain | 2014 | [90] | |
Project Haystack 3 | Hierarchical representation of buildings entities and concepts utilizing tagsets | 2014 | [91] | |
BASont | Building Automation and Monitoring | 2012 | [92] | |
Project Haystack 4 | Hierarchical representation of buildings entities and concepts utilizing tagsets | 2019 | [93] | |
Haystack Tagging Ontology (HTO) | Streamlining Data from IoT based on Project Haystack | 2016 | [94] | |
Brick Schema | Metadata and data points from building advancement and needs based on end-use applications | 2016 | [95] | |
Google Digital Building Ontology | Represent structured information about buildings and building-installed equipment | 2020 | [96] | |
Semantic BMS ontology (SBMS) | BAS-protocol-independent model of intelligent building systems | 2016 | [97] | |
CTRLont | Model of Control Logic in Building Automation Systems | 2017 | [99] | |
Green Button | Building Automation and Monitoring | 2011 | [98] | |
RealEstateCore (REC) | Usage analysis and optimization and presence analysis of a building structure | 2017 | [101] | |
Building Topology Ontology (BOT) | Representation of physical and conceptual objects of a building and the connections between them | 2019 | [102] | |
Building Automation and Control Systems (BACS) | Supports the modeling control behavior in a BAS, physical devices of BAS and their location in the building and connection to technical equipment and appliances | 2017 | [103] | |
Knowledge Model for City (KM4City) | Representation model for city and mobility | 2014 | [104] | |
EM-KPI Ontology | Enhance energy management at district and building levels | 2017 | [105] | |
Facility Smart Grid Information Model | An abstract information model of what the Smart Grid looks like from the perspective of a facility | 2014 | [106] | |
RESPOND | Manage real-time optimal energy dispatching, considering all energy assets on site | 2020 | [107] | |
Occupant Behavior -Centric | DNAs Framework (obXML) | Represent the impact of the behavior of occupants on the building’s energy efficiency | 2015 | [112] |
Occupancy Profile (OP) Ontology | Semantic model for occupancy profile | 2020 | [109] | |
Onto-SB | Human Profile Ontology for Energy Efficiency in Smart Building | 2018 | [110] | |
OnCom | Occupant Thermal Comfort Optimization | 2019 | [111] | |
Audits and Assets Management | Building Energy Data Exchange Specification (BEDES) | Data information gathering and storing based on building’s systems | 2014 | [113] |
Virtual Buildings Information System (VBIS) | Classifies and connects asset data sources and systems | 2020 | [114] | |
Ontology of Property Management (OPM) | Vocabulary for modeling complex assets in a building design environment | 2018 | [115] |
Category | Scope | Architecture Used | Case Study | Year | Ref. |
---|---|---|---|---|---|
Energy Performance Improvement | Reduce the performance gap between the real and simulated data | ifcOWL, SimModel, SSN and custom | Use of simulated and measured KPIs to assess the thermal comfort conditions and the HVAC system performance | 2015 | [137] |
Gather and prepare data streaming from various sources and calculate the building performance | RDF schema and custom ontology | Energy Performance assessment using real-time data streaming in a university building, assessed by building managers and engineers | 2017 | [138] | |
Performance tracking at building and district level | ifcOWL, SimModel and SSN ontology | Nineteen solar houses microgrid | 2019 | [139] | |
Building energy savings | RDF schema | Identify any energy waste in an office zone | 2015 | [140] | |
Support of the selection for efficient and best-cost HVAC systems/the evaluation and prioritization of energy performance values (cooling/heating) consumption | InterfaceOnto | Design phase of an office building | 2015 | [143] | |
Optimize the energy performance | SPORTE2 | Building Energy Performance Optimization of a swimming pool using ANN, Genetic Algorithms, real-time sensors and SWRL rules | 2014 | [156] | |
Optimization problem generation on minimizing comfort dissatisfaction of building users regarding specific parameters and minimizing costs of energy consumption | Custom Ontology | Two office rooms are used to evaluate the scope of the ontology | 2017 | [157] | |
Data Injection | Creation of a BIM-based system that automatically associates and updates thermal property measurements with BIM elements in a gbXML schema | gbXML | Two case studies that the method they proposed minimizes the gap between architectural information in BIM and the real data for energy performance simulation | 2015 | [148] |
Use of gbXML schema to convert semantic information coming from raw point cloud data and use it into energy simulation tools | gbXML | Five existing buildings (three residential and two bank buildings) | 2015 | [149] | |
Use of gbXML framework to store data from big buildings, like factories, in gbXML format, to make it easier to import into simulation tools | gbXML | University’s manufacturing facility | 2018 | [150] | |
Facility Management | Provide modification options to facility managers | gbXML, EnergyPlus | Educational building application of real-time data in building energy simulation modifications | 2017 | [151] |
Creation of semantic relationships between BMS data and building spaces | SSN/SONA and BOT ontologies | Educational building support of data analysis, lacking real-time data that was found to be a challenge in HVAC system control | 2018 | [152] | |
BIM and BMS data connected with the semantic web to assist facility managers | - | - | 2018 | [153] | |
Occupant Behavior-Centric | Targets the occupants in a building and makes suggestions to reduce building energy by their behavior | OPTIMUS, SSN/SONA, Urban Energy Ontology | Use of ontology to provide solutions in energy reduction and comfort increase based on the building’s assessment/application of ontology in a lab in Athens where the building’s energy was reduced in contrary to the year before the ontology was applied | 2018 | [144] |
Modeling tool that takes into consideration occupant behavior | obFMU/DNAs, EnergyPlus | Coupled obFMU with EnergyPlus to model occupant behavior lighting control, to model occupant behavior window action and to model HVAC control | 2016 | [145] | |
Reduce building energy consumption by having as top priority occupant behavior changes and covering their thermal comfort needs | Onto-SB | Residential building with four people, where they apply distinctive characteristics and after they integrate the mechanism that is proposed they conclude with a 40% energy consumption reduction | 2019 | [146] | |
Efficient control of appliances and devices in smart buildings, targeting the occupants’ comfort and energy consumption reduction | Onto-SB | Reduce the energy consumption by altering distinctive characteristics in the scenario and make the simulation process quicker | 2020 | [147] | |
Combination of a wireless sensor network and an emotional state analysis from occupants to calibrate indoor thermal comfort | OnCom | Assessing eleven participants with distinctive characteristics and each one responds to the system’s actions in a different situation with respect to the indoor thermal comfort and the results showed that the mean of users agreed with the system’s decisions | 2019 | [111] | |
Decrease in Reused Ontologies | Context-awareness architecture for managing thermal energy in nZEBs | OWL, SWRL | Showing that SPARQL and Semantic Web Rule Language were compatible with decision making in a building | 2017 | [154] |
Supports the modeling control behavior in a BAS, physical devices of BAS and their location in the building and connection to technical equipment and appliances | BACS, EXPRESS, OSPH, SSN/SOSA, BOT and FSM | Inclusion of a room and the automated control of the windows’ shades using SPARQL queries | 2017 | [155] |
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Lygerakis, F.; Kampelis, N.; Kolokotsa, D. Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review. Energies 2022, 15, 7520. https://doi.org/10.3390/en15207520
Lygerakis F, Kampelis N, Kolokotsa D. Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review. Energies. 2022; 15(20):7520. https://doi.org/10.3390/en15207520
Chicago/Turabian StyleLygerakis, Filippos, Nikos Kampelis, and Dionysia Kolokotsa. 2022. "Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review" Energies 15, no. 20: 7520. https://doi.org/10.3390/en15207520