IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p609-d1579008.html
   My bibliography  Save this article

Electricity Consumption and Efficiency Measures in Public Buildings: A Comprehensive Review

Author

Listed:
  • Aarón Ortiz-Peña

    (Renewable Energy Research Institute, Department of Electrical, Electronic, Automatic and Communications Engineering of ETSII-AB, University of Castilla-La Mancha (UCLM), 02071 Albacete, Spain)

  • Andrés Honrubia-Escribano

    (Renewable Energy Research Institute, Department of Electrical, Electronic, Automatic and Communications Engineering of ETSII-AB, University of Castilla-La Mancha (UCLM), 02071 Albacete, Spain)

  • Emilio Gómez-Lázaro

    (Renewable Energy Research Institute, Department of Electrical, Electronic, Automatic and Communications Engineering of ETSII-AB, University of Castilla-La Mancha (UCLM), 02071 Albacete, Spain)

Abstract

Industrialization and the expansion of service sectors have led to a significant increase in electricity consumption. This rising demand has also been observed in public buildings, which account for a considerable share of total electrical energy use. Coupled with the upward trend in energy prices, this increase has likewise escalated electricity costs in these sectors. The objective of this review is to compile studies that analyze electricity consumption in large public buildings, with a primary focus on universities, as well as works that propose or implement energy-saving measures aimed at reducing consumption. Throughout this review, it is observed that effective monitoring of consumption as well as the use of demand management systems can reduce electricity consumption by up to 15%. Additionally, the studies collected consistently highlight the need for improvements in real-time data monitoring to enhance energy management. Buildings that implement energy-saving measures achieve reductions in demand exceeding 10%, while those incorporating renewable energy systems are capable of covering between 40% and 50% of their energy needs. Of these systems, solar photovoltaic technology is that most widely adopted by public buildings, primarily due to its adaptability to the architectural characteristics and operational requirements of such facilities. This review underscores the substantial impact that optimized monitoring and renewable energy integration can have on reducing the energy footprint of large public facilities.

Suggested Citation

  • Aarón Ortiz-Peña & Andrés Honrubia-Escribano & Emilio Gómez-Lázaro, 2025. "Electricity Consumption and Efficiency Measures in Public Buildings: A Comprehensive Review," Energies, MDPI, vol. 18(3), pages 1-25, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:609-:d:1579008
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/609/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/3/609/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Di Stefano, Julian, 2000. "Energy efficiency and the environment: the potential for energy efficient lighting to save energy and reduce carbon dioxide emissions at Melbourne University, Australia," Energy, Elsevier, vol. 25(9), pages 823-839.
    2. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    3. Csereklyei, Zsuzsanna, 2020. "Price and income elasticities of residential and industrial electricity demand in the European Union," Energy Policy, Elsevier, vol. 137(C).
    4. Li, Raymond & Lee, Hazel, 2022. "The role of energy prices and economic growth in renewable energy capacity expansion – Evidence from OECD Europe," Renewable Energy, Elsevier, vol. 189(C), pages 435-443.
    5. S. M. Shafie & A. H. Nu man & N. N. A. N. Yusuf, 2021. "Strategy in Energy Efficiency Management: University Campus," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 310-313.
    6. Nikolaos Papadakis & Dimitrios Al. Katsaprakakis, 2023. "A Review of Energy Efficiency Interventions in Public Buildings," Energies, MDPI, vol. 16(17), pages 1-34, August.
    7. Fiaschi, Daniele & Bandinelli, Romeo & Conti, Silvia, 2012. "A case study for energy issues of public buildings and utilities in a small municipality: Investigation of possible improvements and integration with renewables," Applied Energy, Elsevier, vol. 97(C), pages 101-114.
    8. Junhui Huang & Sakdirat Kaewunruen, 2023. "Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression," Energies, MDPI, vol. 16(2), pages 1-15, January.
    9. Katzin, David & Marcelis, Leo F.M. & van Mourik, Simon, 2021. "Energy savings in greenhouses by transition from high-pressure sodium to LED lighting," Applied Energy, Elsevier, vol. 281(C).
    10. 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.
    11. Cai, W.G. & Wu, Y. & Zhong, Y. & Ren, H., 2009. "China building energy consumption: Situation, challenges and corresponding measures," Energy Policy, Elsevier, vol. 37(6), pages 2054-2059, June.
    12. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    13. Lee, Jongsung & Chang, Byungik & Aktas, Can & Gorthala, Ravi, 2016. "Economic feasibility of campus-wide photovoltaic systems in New England," Renewable Energy, Elsevier, vol. 99(C), pages 452-464.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhaocheng Li & Yu Song, 2022. "Energy Consumption Linkages of the Chinese Construction Sector," Energies, MDPI, vol. 15(5), pages 1-13, February.
    2. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    3. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
    4. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
    5. 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.
    6. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    7. Ting Jin & Rui Xu & Kunqi Su & Jinrui Gao, 2025. "A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction," Mathematics, MDPI, vol. 13(4), pages 1-23, February.
    8. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    9. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    10. Kazimierz Kawa & Rafał Mularczyk & Waldemar Bauer & Katarzyna Grobler-Dębska & Edyta Kucharska, 2024. "Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings," Energies, MDPI, vol. 17(13), pages 1-16, June.
    11. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    12. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    13. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Optimization design of BCHP system to maximize to save energy and reduce environmental impact," Energy, Elsevier, vol. 35(8), pages 3388-3398.
    14. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    15. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    16. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    17. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    18. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
    19. Sertkaya, Ahmet Ali & Bilir, Şefik & Kargıcı, Suna, 2011. "Experimental investigation of the effects of orientation angle on heat transfer performance of pin-finned surfaces in natural convection," Energy, Elsevier, vol. 36(3), pages 1513-1517.
    20. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:609-:d:1579008. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.