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Article

A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings

1
STI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria
2
Facultad de Informática, Universidad de Murcia, 30100 Murcia, Spain
3
Wageningen Data Competence Center (WDCC), Wageningen University and Research, 6708 PB Wageningen, The Netherlands
4
Consumption & Healthy Lifestyles Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Giacomo Chiesa and Michal Pomianowski
Energies 2022, 15(9), 3155; https://doi.org/10.3390/en15093155
Received: 21 March 2022 / Revised: 11 April 2022 / Accepted: 19 April 2022 / Published: 26 April 2022
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. View Full-Text
Keywords: near-zero energy buildings; energy efficiency; semantic technology; knowledge graphs; energy performance certificates; energy performance certification near-zero energy buildings; energy efficiency; semantic technology; knowledge graphs; energy performance certificates; energy performance certification
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MDPI and ACS Style

Popa, A.; Ramallo González, A.P.; Jaglan, G.; Fensel, A. A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings. Energies 2022, 15, 3155. https://doi.org/10.3390/en15093155

AMA Style

Popa A, Ramallo González AP, Jaglan G, Fensel A. A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings. Energies. 2022; 15(9):3155. https://doi.org/10.3390/en15093155

Chicago/Turabian Style

Popa, Angela, Alfonso P. Ramallo González, Gaurav Jaglan, and Anna Fensel. 2022. "A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings" Energies 15, no. 9: 3155. https://doi.org/10.3390/en15093155

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