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Review of natural language processing techniques for characterizing positive energy districts
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
Dalarna University, School of Information and Engineering, Microdata Analysis.
Dalarna University, School of Information and Engineering, Energy Technology.ORCID iD: 0000-0002-2369-0169
2023 (English)In: journal of Physics; Conference series, Institute of Physics Publishing (IOPP), 2023, Vol. 2600, no 8, article id 082024Conference paper, Published paper (Refereed)
Abstract [en]

The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments. © 2023 Institute of Physics Publishing. All rights reserved.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2023. Vol. 2600, no 8, article id 082024
Keywords [en]
modelling, natural language processing (NLP), NLP task, PED elements, positive energy districts, Energy efficiency, Natural language processing systems, Language processing, Modeling, Natural language processing, Natural language processing task, Natural languages, Positive energies, Positive energy district, Positive energy district element, Modeling languages
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:du-47653DOI: 10.1088/1742-6596/2600/8/082024Scopus ID: 2-s2.0-85180156963OAI: oai:DiVA.org:du-47653DiVA, id: diva2:1823415
Conference
International Conference on the Built Environment in Transition, CISBAT 2023
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved

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Han, MengjieShah, JuveriaZhang, Xingxing

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • chicago-note-bibliography
  • Other style
More styles
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  • de-DE
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf