Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

• Published in: Buildings (Basel) Vol. 14; no. 2; p. 371
• Main Authors: Han, Mengjie, Canli, Ilkim, Shah, Juveria, Zhang, Xingxing, Dino, Ipek Gursel, Kalkan, Sinan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2024
• Subjects: Architecture and energy conservation; Artificial intelligence; Building management systems; characterization; Computational linguistics; Design optimization; Efficiency; Emissions; Emissions (Pollution); Energy balance; Energy consumption; Energy efficiency; Energy industry; Energy management; Energy modeling; Environmental engineering; Green buildings; Innovations; Language; Language processing; Learning algorithms; Machine learning; Mapping; Methods; Modelling; Natural language interfaces; Natural language processing; Pilot projects; Positive Energy District; Simulation; State-of-the-art reviews; Urban environments; Europe; Positive Energy District; natural language processing; machine learning; characterization
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14020371

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.