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Review of natural language processing techniques for characterizing positive energy districts

Journal of physics. Conference series, 2023, Vol.2600 (8), p.82024 [Peer Reviewed Journal]

ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2600/8/082024

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  • Title:
    Review of natural language processing techniques for characterizing positive energy districts
  • Author: Han, Mengjie ; Shah, Juveria ; Zhang, Xingxing
  • Subjects: Energy efficiency ; Language processing ; Modeling ; Modeling languages ; modelling ; Natural language processing ; natural language processing (NLP) ; Natural language processing systems ; Natural language processing task ; Natural languages ; NLP task ; PED elements ; Positive energies ; Positive energy district ; Positive energy district element ; positive energy districts
  • Is Part Of: Journal of physics. Conference series, 2023, Vol.2600 (8), p.82024
  • Description: Abstract 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.
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2600/8/082024
  • Source: Open Access: IOP Publishing Free Content
    Geneva Foundation Free Medical Journals at publisher websites
    SWEPUB Freely available online
    ProQuest Central
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