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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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