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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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    as
    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    3. Sorgato, M.J. & Schneider, K. & Rüther, R., 2018. "Technical and economic evaluation of thin-film CdTe building-integrated photovoltaics (BIPV) replacing façade and rooftop materials in office buildings in a warm and sunny climate," Renewable Energy, Elsevier, vol. 118(C), pages 84-98.
    4. Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "Assessment of climate change impact on building energy use and mitigation measures in subtropical climates," Energy, Elsevier, vol. 36(3), pages 1404-1414.
    5. Sonia Jerez & Isabelle Tobin & Robert Vautard & Juan Pedro Montávez & Jose María López-Romero & Françoise Thais & Blanka Bartok & Ole Bøssing Christensen & Augustin Colette & Michel Déqué & Grigory Ni, 2015. "The impact of climate change on photovoltaic power generation in Europe," Nature Communications, Nature, vol. 6(1), pages 1-8, December.
    6. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    7. Chung, William, 2012. "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 95(C), pages 45-49.
    8. Nugent, Daniel & Sovacool, Benjamin K., 2014. "Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey," Energy Policy, Elsevier, vol. 65(C), pages 229-244.
    9. Wan, Kevin K.W. & Li, Danny H.W. & Pan, Wenyan & Lam, Joseph C., 2012. "Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications," Applied Energy, Elsevier, vol. 97(C), pages 274-282.
    10. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    11. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
    12. Khuram Pervez Amber & Muhammad Waqar Aslam & Anzar Mahmood & Anila Kousar & Muhammad Yamin Younis & Bilal Akbar & Ghulam Qadar Chaudhary & Syed Kashif Hussain, 2017. "Energy Consumption Forecasting for University Sector Buildings," Energies, MDPI, vol. 10(10), pages 1-18, October.
    13. Aste, Niccolò & Del Pero, Claudio & Leonforte, Fabrizio & Manfren, Massimiliano, 2013. "A simplified model for the estimation of energy production of PV systems," Energy, Elsevier, vol. 59(C), pages 503-512.
    14. Kabdrakhmanova, Marzhan & Memon, Shazim Ali & Saurbayeva, Assemgul, 2021. "Implementation of the panel data regression analysis in PCM integrated buildings located in a humid subtropical climate," Energy, Elsevier, vol. 237(C).
    15. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    16. Rubio-Bellido, Carlos & Pérez-Fargallo, Alexis & Pulido-Arcas, Jesús A., 2016. "Optimization of annual energy demand in office buildings under the influence of climate change in Chile," Energy, Elsevier, vol. 114(C), pages 569-585.
    17. Ma, Wei Wu & Rasul, M.G. & Liu, Gang & Li, Min & Tan, Xiao Hui, 2016. "Climate change impacts on techno-economic performance of roof PV solar system in Australia," Renewable Energy, Elsevier, vol. 88(C), pages 430-438.
    18. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
    19. Troup, Luke & Eckelman, Matthew J. & Fannon, David, 2019. "Simulating future energy consumption in office buildings using an ensemble of morphed climate data," Applied Energy, Elsevier, vol. 255(C).
    20. Graditi, G. & Ferlito, S. & Adinolfi, G., 2016. "Comparison of Photovoltaic plant power production prediction methods using a large measured dataset," Renewable Energy, Elsevier, vol. 90(C), pages 513-519.
    21. Lam, Joseph C. & Wan, Kevin K.W. & Lam, Tony N.T. & Wong, S.L., 2010. "An analysis of future building energy use in subtropical Hong Kong," Energy, Elsevier, vol. 35(3), pages 1482-1490.
    22. Dino, Ipek Gürsel & Meral Akgül, Cagla, 2019. "Impact of climate change on the existing residential building stock in Turkey: An analysis on energy use, greenhouse gas emissions and occupant comfort," Renewable Energy, Elsevier, vol. 141(C), pages 828-846.
    23. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach," Applied Energy, Elsevier, vol. 240(C), pages 386-398.
    24. Sisman, Nuri & Kahya, Emin & Aras, Nil & Aras, Haydar, 2007. "Determination of optimum insulation thicknesses of the external walls and roof (ceiling) for Turkey's different degree-day regions," Energy Policy, Elsevier, vol. 35(10), pages 5151-5155, October.
    25. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    26. Jo, J.H. & Otanicar, T.P., 2011. "A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems," Renewable Energy, Elsevier, vol. 36(11), pages 2992-3000.
    27. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    28. Bektas Ekici, Betul & Aytac Gulten, Ayca & Aksoy, U. Teoman, 2012. "A study on the optimum insulation thicknesses of various types of external walls with respect to different materials, fuels and climate zones in Turkey," Applied Energy, Elsevier, vol. 92(C), pages 211-217.
    29. Dias, César Luiz de Azevedo & Castelo Branco, David Alves & Arouca, Maurício Cardoso & Loureiro Legey, Luiz Fernando, 2017. "Performance estimation of photovoltaic technologies in Brazil," Renewable Energy, Elsevier, vol. 114(PB), pages 367-375.
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