ANALYSIS OF ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS IN A MODERATE CLIMATE
DOI:
https://doi.org/10.32782/IT/2024-2-23Keywords:
energy management, energy consumption patterns, energy comparative analysis, energy savings, occupancy patterns, energy demandsAbstract
This study provides a comprehensive analysis of energy consumption patterns in various commercial buildings located in regions with a moderate climate, characterized by cold, often frigid winters and hot, humid summers. The aim of this work is to identify operational energy consumption trends across different building types, including offices, healthcare facilities, educational institutions, hotels, and restaurants, to create future energy management and decision-making systems. The methodology of the research employs a comparative analysis approach, examining energy usage across diverse building types. Data was normalized to kilowatt-hours per square foot to facilitate direct comparisons. The analysis considered seasonal variations, incorporating data on external temperature, occupancy patterns, and operational hours. Scientific novelty. The originality of this study lies in its detailed integration of various building types. By focusing on a specific geographic area, this research provides tailored insights into how local climate conditions affect energy use. The study also innovates by translating its findings into practical recommendations for developing machine learning models that can optimize energy usage. Conclusions. The analysis revealed that larger buildings, such as hospitals and large offices, exhibit higher energy usage compared to smaller buildings, due to their extensive facilities and continuous operation. Seasonal trends show significant variations, with peaks during winter and summer due to heating and cooling demands. The study concludes that understanding these patterns is crucial for effective energy management and sustainability efforts. By identifying key parameters influencing energy consumption, this research supports the development of predictive models that can enhance energy efficiency in commercial buildings. The findings offer valuable insights for stakeholders, including building managers, policymakers, and researchers, aiming to reduce energy consumption and improve sustainability in energy management.
References
The impacts of occupant behavior on building energy consumption: A review / S. Chen et al. Sustainable Energy Technologies and Assessments. 2021. Vol. 45. P. 1–10. URL: https://doi.org/10.1016/j.seta.2021.101212(date of access: 04.06.2024).
Jia M., Srinivasan R. S., Raheem A. A. From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renewable and Sustainable Energy Reviews. 2017. Vol. 68. P. 525–540. URL: https://doi.org/10.1016/j.rser.2016.10.011 (date of access: 04.06.2024).
Amasyali K., El-Gohary N. M. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews. 2018. Vol. 81. P. 1192–1205. URL: https://doi.org/10.1016/j.rser.2017.04.095 (date of access: 04.06.2024).
Klinger B. A., Ryan S. J. Population distribution within the human climate niche. PLOS Climate. 2022. Vol. 1, no. 11. P. 1–10. URL: https://doi.org/10.1371/journal.pclm.0000086 (date of access: 04.06.2024).
Sadaghat B., Afzal S., Khiavi A. J. Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach. Expert Systems with Applications. 2024. Vol. 251. P. 1–10. URL: https://doi.org/10.1016/j.eswa.2024.123934 (date of access: 04.06.2024).
National Renewable Energy Laboratory. AWS S3 Explorer for the Open Energy Data Initiative. Open Energy Data Initiative (OEDI). URL: https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pdsbuilding-stock/end-use-load-profiles-for-us-building-stock/2023/comstock_amy2018_release_2/timeseries_aggregates/by_state/upgrade=18/state=MN/ (date of access: 04.06.2024).
