A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings
Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/9/3155 |
| _version_ | 1797505018102808576 |
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| author | Angela Popa Alfonso P. Ramallo González Gaurav Jaglan Anna Fensel |
| author_facet | Angela Popa Alfonso P. Ramallo González Gaurav Jaglan Anna Fensel |
| author_sort | Angela Popa |
| collection | DOAJ |
| description | Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy consumption values are enough to determine a dwellings’ energy performance rating. The essential outcome of this work is a data-driven prediction tool for energy performance labels that can run automatically. The tool is based on the cutting edge kNN classification algorithm and trained on open datasets with actual building data such as those coming from the IoT paradigm. Additionally, it assesses the results of the prediction by analysing its accuracy values. Furthermore, an approach to semantic annotations for energy performance certification data with currently available ontologies is presented. Use cases for an extension of this work are also discussed in the end. |
| first_indexed | 2024-03-10T04:12:40Z |
| format | Article |
| id | doaj.art-0039c8715b114c349357e0fb8b0f4947 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:12:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0039c8715b114c349357e0fb8b0f49472023-11-23T08:07:19ZengMDPI AGEnergies1996-10732022-04-01159315510.3390/en15093155A Semantically Data-Driven Classification Framework for Energy Consumption in BuildingsAngela Popa0Alfonso P. Ramallo González1Gaurav Jaglan2Anna Fensel3STI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, AustriaFacultad de Informática, Universidad de Murcia, 30100 Murcia, SpainSTI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, AustriaSTI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, AustriaEncouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy consumption values are enough to determine a dwellings’ energy performance rating. The essential outcome of this work is a data-driven prediction tool for energy performance labels that can run automatically. The tool is based on the cutting edge kNN classification algorithm and trained on open datasets with actual building data such as those coming from the IoT paradigm. Additionally, it assesses the results of the prediction by analysing its accuracy values. Furthermore, an approach to semantic annotations for energy performance certification data with currently available ontologies is presented. Use cases for an extension of this work are also discussed in the end.https://www.mdpi.com/1996-1073/15/9/3155near-zero energy buildingsenergy efficiencysemantic technologyknowledge graphsenergy performance certificatesenergy performance certification |
| spellingShingle | Angela Popa Alfonso P. Ramallo González Gaurav Jaglan Anna Fensel A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings Energies near-zero energy buildings energy efficiency semantic technology knowledge graphs energy performance certificates energy performance certification |
| title | A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings |
| title_full | A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings |
| title_fullStr | A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings |
| title_full_unstemmed | A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings |
| title_short | A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings |
| title_sort | semantically data driven classification framework for energy consumption in buildings |
| topic | near-zero energy buildings energy efficiency semantic technology knowledge graphs energy performance certificates energy performance certification |
| url | https://www.mdpi.com/1996-1073/15/9/3155 |
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