Smart building and Digital Twin

This Papaer/Work was from 2018 but never publish,
A building energy cognitive performance indicator ontology

Smart cities have smart buildings. The buildings will be intelligent and autonomous, and they exchange messages with another element of Smart city. Ontologies provide the framework for interoperability and the standards to establish the skeleton of the smart buildings. Likewise, the integration of the key performance indicator to the smart building for self-evaluation is considerable. The integration from inhomogeneous from different data sources to calculate the KPI

The Building infrastructure consumes 30% of total energy and 60% of electrical energy every year[reference]. With the increase of the device we install to the buildings, the consumption will increase. Improvement of the energy-efficient can reduce the energy demand, the operational cost and curb emission—the local cities working to decrease the environmental footprint. Moreover, improve the air quality and keep the occupants healthy. Energy management (EM) is vital and has a goal to exploit energy and reduce the cost. Likewise, the energy now produced distributed with many renewable energy sources; energy management is difficult; we have to manage the energy from these sources. Information and communication technology (ICT) helps the EM and integrate with energy systems. Those systems are the Smart Grids. Smart grids reduce the cost of energy. Moreover, The Smart Grids can inform the residents of buildings for their energy habits and urges them to change if they need it, Also helps avoid overloads, inform and notify for the feature loads in Network. There is much application that improves the operation of power grids. There are many energy domains with its ontology. These ontologies represent the energy knowledge improvements are deployed in specific smart grid scenarios. The key performance indicator (KPI) helps the EM reduce the cost; if we have deviation, we could change the energy strategy. The proper defined KPIs provides comparable data and helps the evaluation of the system. Moreover, the use of KPIs makes evaluation transport for everyone. The KPIs need data from energy consumption, power loads, or “static” data like the building properties. The collection of data from sensors or devices at the buildings may be stored in different locations. Utilizing these heterogeneous data sources has research interest and names as linked data from different sources—the integration and use of semantic web advice to represent and store the data as a triplestore. Moreover, an ontology helps integrate data from different sources provides explicit mapping and uniqueness.

This section describes the structure of BECPIO, figure 1. BECPIO implemented with protégé ontology editor. For more information could see the recognition ontology home page. The BECPIO is a representation environment for smart grid at buildings and integration with real-time data from IoT sensors and describes building entity relationships. To drive open and having interoperability we use the Web ontology language (OWL), an open modelling language. The BECPIO is the common language that will enable control of building devices and development services. It uses and pragmatically maps present standards ontologies. The BECIPIO does not try to re-invent a new model but maps and reusing the existing models to standards energy building models. Also, enable the property owner to make their building inhabitants of the smart city and interconnected with other smart city components, for example, the public transportation system to inform the building API when there is traffic at the roads. The most important at the ontology is representing the business case and the energy optimization; there are many standards for energy Domain the We create a first version of the model from Saref family ontology, but it can expand when need it with other classes in this way we were describing.
Making a knowledge graph to the building to connect it is extremely useful to export knowledge and exchange it with other Smart application programming interfaces—

the property owners is vital to control the knowledge graph and their Data

. Moreover, there is a capability to connect to other knowledge graphs, which creates value. We can use the knowledge graph for other artificially intelligent systems. The telemetry data are collected from the building and are available for analysis and application. BECPIO enables linking to objects in external systems using rich namespace Alias and mapping to standards. Every building entity has a standard IRI that allow for interoperability with other systems. (alignment to other ontology (brick, waste management, Access control .. we will see this) There is Documentation at the link of BECPIO(purl.org/recognition). The BECIPIO can represent anything as Building Device. Every building has a geographical system point. Also, every device could be a Spatial Thing.

The work was made with owl in python and postgress but for now i think the most aproprate tool is the neo4j.