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The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @en
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OWL ontology for the IFC conceptual data schema and exchange file format for Building Information Model (BIM) data @en
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The aim of the Occupant Feedback Ontology is to semantically describe passive and active occupant feedback and to enable integration of this feedback with linked building data. @en
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The Ontology for Property Management (OPM) extends the concepts introduced in the Smart Energy Aware Systems (SEAS) Evaluations ontology. @en
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This ontology describes the components, failures, sensors, and events related to offshore wind platforms. @en
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The REACT ontology aims to represent all the necessary knowledge to support the achievement of island energy independence through renewable energy generation and storage, a demand response platform, and promoting user engagement in a local energy community. The REACT ontology has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. @en
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Smart Building Evacuation Ontology (SBEO) is an ontology that couples the information about any building with its occupants such that it can be used in many useful ways. For example, indoor localization of people, detection of any hazard, a recommendation of normal routes such as shopping or stadium seating routes, or safe and feasible emergency evacuation routes or both of them all together. The core SBEO covers the concepts related to the geometry of building, devices and components of the building, route graphs correspondent to the building topology, users' characteristics and preferences, situational awareness of both building (hazard detection, status of routes in terms of availability and occupancy) and users (tracking, management of groups, status in terms of fitness), and emergency evacuation. @en
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This ontology defines batteries and their state of charge ratio property. @en
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The SEAS Device ontology defines `seas:Device` as physical system that are designed to execute one or more procedures that involve the physical world. @en
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The SEAS Forecasting ontology extends the [Procedure Execution ontology (PEP)](https://w3id.org/pep/) @en
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This ontology defines: - a set of subclasses of `seas:Evaluation` to better interpret evaluations of quantifiable properties. - a set of sub properties of `seas:hasProperty` to qualify time-related properties. @en
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SemTS is an ontology designed to identify and describe segments within time series data, which are specific data points or intervals that can overlap. These segments encompass characteristic knowledge about the time interval they cover, including common time series features, structural anomalies, motifs, or information provided by domain experts. By classifying and semantically representing this knowledge, SemTS promotes organized reusability and efficient propagation, potentially reducing resource expenditure while enhancing future analyses. It employs established semantic approaches. Examples are DCAT to reference associated time series data, OWL-Time to define the index structure of time series data and segments or ML-Schema to expand the expressiveness regarding data analysis task information. SemTS's design involves categorizing time series knowledge and mapping it to specific intervals and dimensions of time series data. It introduces a class called TimeSeriesSegment to model these segments, extending the DCAT Dataset class to enable segments to be part of other segments. This structure allows for the association of knowledge, such as anomalies, with particular intervals or data points. TimeIndex specifications extend OWL-Time classes, while dimensional details are represented by DataDimension. The segment-wise consideration of knowledge indirectly serves as an index structure, linking meaningful time series data with categorized knowledge. At the highest level of abstraction, time series knowledge is divided into three categories: DataKnowledge, ScenarioKnowledge, and MethodKnowledge. DataKnowledge refers to insights extracted directly from the data or through analytical methods, such as class membership from time series clustering. ScenarioKnowledge describes verified contexts, including data annotations or domain-specific process knowledge, often equating to expert-provided a priori information and can also define facts derived from inferred knowledge. MethodKnowledge encompasses effective analytical method presets or mathematical/logical equivalents of established process information. @en
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A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en
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The Wind Farm Ontology (wfont) describes wind farms and their components. It is inspired by the SANDIA Report SAND2009-1171 and DAEKIN project outcomes. It reuses the AffectedBy and EEP (Execution-Executor-Procedure) ontology design patterns to discover sensors or actuators that observe or act on a given quality or feature of interest. @en
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Smart home ontology for weather phenomena and exterior conditions @en