Machine learning based prediction of long-term energy consumption and overheating under climate change impacts using urban building energy modeling

Akyol I. C., Halacli E. G., Uçar S., Iseri O. K., Yavuz F., Güney D., ...Daha Fazla

SUSTAINABLE CITIES AND SOCIETY, cilt.130, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 130
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.scs.2025.106500
  • Dergi Adı: SUSTAINABLE CITIES AND SOCIETY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Geobase, INSPEC
  • Anahtar Kelimeler: Climate change impacts, Energy use prediction, Facade retrofit, Indoor overheating, Machine learning, Simulation, Urban building energy modeling (UBEM)
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In cities, well-informed decisions targeting improved building energy performance under climate change impacts require tools that can make long-term projections and devise effective strategies. Physics-based Urban Building Energy Models (UBEM) can calculate building performance for future years; however, this process is challenging as (i) future weather files are generated only for discrete years, and (ii) physics-based simulations are computationally demanding, hindering the evaluation of a high number of buildings for all future years. Alternatively, machine learning (ML) approaches can offer high-precision estimations at a lower computational cost. In this paper, a UBEM-assisted ML-based approach that predicts residential buildings' heating energy use and indoor overheating for the current and future years is proposed. A UBEM of a residential district is developed, and simulations are performed using weather files of the current year, 2050, and 2080 to develop training/testing datasets. Multi-layer perceptrons are trained to a very high predictive performance (with an R-2 score of 0.98 and 0.96 for the two output features), with a remarkable speed advantage (similar to 430 times faster than simulations). Finally, the results of the long-term analysis of three urban-scale retrofit scenarios are presented, which offers insights into the potential use of the proposed ML models.