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Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
Department of Architecture, Middle East Technical University, Ankara 06800, Türkiye;Center for Solar Energy Research and Applications (ODTÜ-GÜNAM), Middle East Technical University, Ankara 06800, Türkiye.ORCID iD: 0000-0001-5166-5676
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0009-0000-0066-4585
Dalarna University, School of Information and Engineering, Energy Technology.ORCID iD: 0000-0002-2369-0169
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2024 (English)In: Buildings, E-ISSN 2075-5309, Vol. 14, no 2, article id 371Article in journal (Refereed) Published
Abstract [en]

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 14, no 2, article id 371
Keywords [en]
Positive Energy District; machine learning; natural language processing; characterization
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:du-48012DOI: 10.3390/buildings14020371ISI: 001172199400001Scopus ID: 2-s2.0-85185706786OAI: oai:DiVA.org:du-48012DiVA, id: diva2:1836727
Funder
Vinnova, P2022-01000Swedish Energy Agency, 8569501Available from: 2024-02-10 Created: 2024-02-10 Last updated: 2024-03-18Bibliographically approved

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Han, MengjieShah, JuveriaZhang, Xingxing

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