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Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios

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  • dos Santos Ferreira, Greicili
  • Martins dos Santos, Deilson
  • Luciano Avila, Sérgio
  • Viana Luiz Albani, Vinicius
  • Cardoso Orsi, Gustavo
  • Cesar Cordeiro Vieira, Pedro
  • Nilson Rodrigues, Rafael

Abstract

Understanding and predicting power consumption behavior helps estimate costs, seek actions to save energy and plan affirmative actions that raise people's awareness. Organized civil society has made efforts in this context. Our contribution is a framework that first extracts knowledge about consumption through an extensive time series analysis of the building under study. Proper acquisition of meteorological (e. g. dry bulb temperature, humidity, solar radiation, and precipitation) and power consumption data are discussed. The correlation between these time series is verified, which develops knowledge about the problem. We believe forecasts will be more reliable using accurate models with more knowledge. Afterward, we forecast for the short-term period (hour, day and month-ahead) and long-term period (2030 and 2050), considering the scenarios proposed by the World Energy Outlook (WEO-2022) and Convention on Climate Change (COP27). Acquisition, processing, data analysis, as well as predictions, each framework step follows the International Performance Measurement and Verification Protocol (IPMVP-2022) recommendations. Building energy consumption forecasts considering IPMVP, WEO, and COP27 scenarios are originals in the literature. IEEE rigor for load measurement and modeling is achieved. Our study also follows the ISO:50000 energy efficiency purposes. The analysis and forecasting use data from a public building with an approximate circulation of 5,000 people per day. The objective is to contribute to a better understanding of building engineering to structure energy management actions. As stated at COP27: enough greenwashing talk, let us move on to smart actions.

Suggested Citation

  • dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003446
    DOI: 10.1016/j.apenergy.2023.120980
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    1. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    2. Ludovica Maria Campagna & Francesco Fiorito, 2022. "On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis," Energies, MDPI, vol. 15(1), pages 1-35, January.
    3. Michal Pavlicko & Mária Vojteková & Oľga Blažeková, 2022. "Forecasting of Electrical Energy Consumption in Slovakia," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    4. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    5. Baglivo, Cristina & Congedo, Paolo Maria & Murrone, Graziano & Lezzi, Dalila, 2022. "Long-term predictive energy analysis of a high-performance building in a mediterranean climate under climate change," Energy, Elsevier, vol. 238(PA).
    6. D'Agostino, D. & Parker, D. & Epifani, I. & Crawley, D. & Lawrie, L., 2022. "How will future climate impact the design and performance of nearly zero energy buildings (NZEBs)?," Energy, Elsevier, vol. 240(C).
    7. Turki Alajmi & Patrick Phelan, 2020. "Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach," Energies, MDPI, vol. 13(8), pages 1-19, April.
    8. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    9. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    10. Baak, M. & Koopman, R. & Snoek, H. & Klous, S., 2020. "A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    11. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    12. Olamide Jogunola & Bamidele Adebisi & Khoa Van Hoang & Yakubu Tsado & Segun I. Popoola & Mohammad Hammoudeh & Raheel Nawaz, 2022. "CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-16, January.
    13. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    14. Lee, Soo-Jin & Song, Seung-Yeong, 2022. "Time-series analysis of the effects of building and household features on residential end-use energy," Applied Energy, Elsevier, vol. 312(C).
    15. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    16. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    17. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    18. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    19. Yang, Xinyan & Zhang, Shicong & Xu, Wei, 2019. "Impact of zero energy buildings on medium-to-long term building energy consumption in China," Energy Policy, Elsevier, vol. 129(C), pages 574-586.
    20. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    21. Zekić-Sušac, Marijana & Mitrović, Saša & Has, Adela, 2021. "Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities," International Journal of Information Management, Elsevier, vol. 58(C).
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    1. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
    2. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).

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