Machine learning based prediction of long-term energy consumption and overheating under climate change impacts using urban building energy modeling
SUSTAINABLE CITIES AND SOCIETY, cilt.130, 2025 (SCI-Expanded, Scopus)
- 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.