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Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey

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  • Tamer, Tolga
  • Gürsel Dino, Ipek
  • Meral Akgül, Cagla

Abstract

This research presents a methodological framework for lifetime energy demand and PV energy generation predictions for a given building considering the CC impacts through multivariate regression models. As a case study, a hypothetical office building in Turkey was selected. An existing linear morphing methodology was utilized to generate future weather files for all 81 cities in Turkey. For each year and city, corresponding weather metrics were calculated, and heating/cooling demand and PV energy generation values were computed through building energy simulations. Obtained data were used to develop two sets of multivariate regression models: (i) models to predict future weather metrics and (ii) models to predict future energy demand and generation. These models allowed lifetime energy demand and generation analysis (including associated GWP and cost) of the building considering CC impacts using only the current weather metrics of its location. For a lifetime of 60 years, considering CC impacts yielded substantially higher cooling (averaging at +0.5 MWh/m2 in the warmest region) and lower heating loads (averaging at −0.4 MWh/m2 in the coldest region). For Turkey, the carbon intensity and the unit cost of cooling are much higher than those of heating. Therefore, the shift from heating to cooling has significant consequences in lifetime GWP and cost values (averaging +212 kg CO2-eq/m2 and +27 $/m2, respectively, for the warmest region), emphasizing the importance of the decarbonization of the energy sector. The impact of CC on PV energy generation was limited (all-city average of +0.02 MWh/m2 for the building lifetime). Our regression-based approach can be further expanded to include not only various building parameters and types, but also supply-demand matching potentials.

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  • Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:rensus:v:162:y:2022:i:c:s1364032122003069
    DOI: 10.1016/j.rser.2022.112396
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    2. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    3. Zhou, Yuekuan & Zheng, Siqian, 2024. "A co-simulated material-component-system-district framework for climate-adaption and sustainability transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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