IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v229y2026ics1364032125012791.html

A review of methods and techniques in building energy management systems for energy efficiency enhancement

Author

Listed:
  • Gonzales-Zurita, Oscar
  • Díaz-Bedoya, Daniel
  • González-Rodríguez, Mario
  • Clairand, Jean-Michel

Abstract

Building Energy Management Systems (BEMS) are known as technological platforms that manage energy efficiency and sustainability in buildings. These technologies integrate control of heating, cooling, lighting, and other services, making optimum use of these resources. As governments worldwide prioritize energy efficiency, often through policies that encourage reduced consumption, BEMS have become increasingly important to achieve these goals. In this paper, the authors perform research on BEMS focused on improving energy efficiency in buildings. While earlier reviews have covered parts of this topic, our analysis uncovers several gaps in the literature that suggest promising lines of inquiry for future work on BEMS, addressing their technologies employed to improve energy efficiency. Additionally, this research discusses how recent breakthroughs, like artificial intelligence and machine learning, are creating fresh opportunities for innovation in BEMS design. Although these technologies expand what today’s systems can do, they also introduce new research challenges that must be addressed. This review’s goal is to build a strong foundation for the next generation of smarter, more sustainable control systems in BEMS.

Suggested Citation

  • Gonzales-Zurita, Oscar & Díaz-Bedoya, Daniel & González-Rodríguez, Mario & Clairand, Jean-Michel, 2026. "A review of methods and techniques in building energy management systems for energy efficiency enhancement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:rensus:v:229:y:2026:i:c:s1364032125012791
    DOI: 10.1016/j.rser.2025.116606
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032125012791
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2025.116606?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cai, Helen & Zhang, Wanhao & Yuan, Qiong & Salameh, Anas A. & Alahmari, Saad & Ferrara, Massimiliano, 2025. "Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support," Energy Economics, Elsevier, vol. 142(C).
    2. Kaabinejadian, Amirreza & Pozarlik, Artur & Acar, Canan, 2025. "A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems," Applied Energy, Elsevier, vol. 379(C).
    3. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    4. Haritza Camblong & Irati Zapirain & Octavian Curea & Juanjo Ugartemendia & Zina Boussaada & Ramon Zamora, 2024. "Rule-Based Energy Management System to Enhance PV Self-Consumption in a Building: A Real Case," Energies, MDPI, vol. 17(23), pages 1-17, December.
    5. Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Maher Abuhussain & Yakubu Aminu Dodo & Saleh Alhumaid & Ali Hussain Alhamami, 2025. "Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities," Energies, MDPI, vol. 18(6), pages 1-22, March.
    6. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    7. Chaovarit Janpirom & Wachirawictch Nilsook & Aekkarat Suksukont, 2025. "Development of hybrid CNN-LSTM for non-intrusive load monitoring," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(2), pages 575-582.
    8. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer energy trading of net-zero energy communities with renewable energy systems integrating hydrogen vehicle storage," Applied Energy, Elsevier, vol. 298(C).
    9. Xu, Shuang & Zhao, Yong & Li, Yuanzheng & Zhou, Yue, 2021. "An iterative uniform-price auction mechanism for peer-to-peer energy trading in a community microgrid," Applied Energy, Elsevier, vol. 298(C).
    10. Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
    11. Anissa Nurdiawati & Frauke Urban, 2021. "Towards Deep Decarbonisation of Energy-Intensive Industries: A Review of Current Status, Technologies and Policies," Energies, MDPI, vol. 14(9), pages 1-33, April.
    12. M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    13. Afzal, Sadegh & Ziapour, Behrooz M. & Shokri, Afshar & Shakibi, Hamid & Sobhani, Behnam, 2023. "Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms," Energy, Elsevier, vol. 282(C).
    14. Faiza Qayyum & Harun Jamil & Faiyaz Ali, 2023. "A Review of Smart Energy Management in Residential Buildings for Smart Cities," Energies, MDPI, vol. 17(1), pages 1-29, December.
    15. GhaffarianHoseini, Ali & Zhang, Tongrui & Nwadigo, Okechukwu & GhaffarianHoseini, Amirhosein & Naismith, Nicola & Tookey, John & Raahemifar, Kaamran, 2017. "Application of nD BIM Integrated Knowledge-based Building Management System (BIM-IKBMS) for inspecting post-construction energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 935-949.
    16. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    17. Li, Qing & Zhang, Lianying & Zhang, Limao & Wu, Xianguo, 2021. "Optimizing energy efficiency and thermal comfort in building green retrofit," Energy, Elsevier, vol. 237(C).
    18. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    19. Michał Turski & Agnieszka Jachura, 2022. "Life Cycle Assessment of Dispersed Phase Change Material Heat Accumulators for Cooperation with Buildings in the District Heating System," Energies, MDPI, vol. 15(16), pages 1-24, August.
    20. Sadeghi, Delnia & Ahmadi, Seyed Ehsan & Amiri, Nima & Satinder, & Marzband, Mousa & Abusorrah, Abdullah & Rawa, Muhyaddin, 2022. "Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings," Energy, Elsevier, vol. 253(C).
    21. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    22. Oleg Rekutov & Michail Surkov & Danil Lyapunov & Alexey Muravlev & Alexandra Pravikova & Anton Yudintsev & Victor Rulevskiy & Oleg Bubnov & Victor Pchelnikov, 2022. "Simulators for Designing Energy-Efficient Power Supplies Based on Solar Panels," Energies, MDPI, vol. 15(7), pages 1-22, March.
    23. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    24. Tekler, Zeynep Duygu & Low, Raymond & Zhou, Yuren & Yuen, Chau & Blessing, Lucienne & Spanos, Costas, 2020. "Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications," Applied Energy, Elsevier, vol. 275(C).
    25. Alessandro Cannavale & Ubaldo Ayr & Francesco Fiorito & Francesco Martellotta, 2020. "Smart Electrochromic Windows to Enhance Building Energy Efficiency and Visual Comfort," Energies, MDPI, vol. 13(6), pages 1-17, March.
    26. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    27. Kim, Jangkyum & Yoo, Yoon-Sik & Yang, Hyo Sik & Choi, Ho Seon, 2024. "Robust uncertainty-aware control of energy storage systems using biased renewable energy forecast," Applied Energy, Elsevier, vol. 367(C).
    28. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    29. Younès Dagdougui & Ahmed Ouammi & Rachid Benchrifa, 2020. "Energy Management-Based Predictive Controller for a Smart Building Powered by Renewable Energy," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    30. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    31. Nilofar Asim & Marzieh Badiei & Masita Mohammad & Halim Razali & Armin Rajabi & Lim Chin Haw & Mariyam Jameelah Ghazali, 2022. "Sustainability of Heating, Ventilation and Air-Conditioning (HVAC) Systems in Buildings—An Overview," IJERPH, MDPI, vol. 19(2), pages 1-16, January.
    32. Das, Anooshmita & Annaqeeb, Masab Khalid & Azar, Elie & Novakovic, Vojislav & Kjærgaard, Mikkel Baun, 2020. "Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods," Applied Energy, Elsevier, vol. 269(C).
    33. Marta Gangolells & Miquel Casals & Marcel Macarulla & Núria Forcada, 2021. "Exploring the Potential of a Gamified Approach to Reduce Energy Use and Carbon Emissions in the Household Sector," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    34. Dorota Chwieduk & Wojciech Bujalski & Bartosz Chwieduk, 2020. "Possibilities of Transition from Centralized Energy Systems to Distributed Energy Sources in Large Polish Cities," Energies, MDPI, vol. 13(22), pages 1-23, November.
    35. Darya Nemova & Evgeny Kotov & Darya Andreeva & Svyatoslav Khorobrov & Vyacheslav Olshevskiy & Irina Vasileva & Daria Zaborova & Tatiana Musorina, 2022. "Experimental Study on the Thermal Performance of 3D-Printed Enclosing Structures," Energies, MDPI, vol. 15(12), pages 1-22, June.
    36. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    37. Pouria Bahramnia & Seyyed Mohammad Hosseini Rostami & Jin Wang & Gwang-jun Kim, 2019. "Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control," Energies, MDPI, vol. 12(24), pages 1-24, December.
    38. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Missing data imputation using mixture factor analysis for building electric load data," Applied Energy, Elsevier, vol. 304(C).
    39. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    40. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    41. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    42. Xia, Yuanxing & Xu, Qingshan & Tao, Siyu & Du, Pengwei & Ding, Yixing & Fang, Jicheng, 2022. "Preserving operation privacy of peer-to-peer energy transaction based on Enhanced Benders Decomposition considering uncertainty of renewable energy generations," Energy, Elsevier, vol. 250(C).
    43. Zhang, Heng & Zhang, Shenxi & Hu, Xiao & Cheng, Haozhong & Gu, Qingfa & Du, Mengke, 2022. "Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion," Applied Energy, Elsevier, vol. 306(PB).
    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. Zhang, Chaobo & Zhang, Jian & Zhao, Yang & Lu, Jie, 2025. "Automated data-driven building energy load prediction method based on generative pre-trained transformers (GPT)," Energy, Elsevier, vol. 318(C).
    2. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    3. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    4. Tariq, Shahzeb & Ali, Usama & Kim, Sangyoun & Yoo, ChangKyoo, 2025. "Multi-agent distributed reinforcement learning for energy-efficient thermal comfort control in multi-zone buildings with diverse occupancy patterns," Energy, Elsevier, vol. 332(C).
    5. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    6. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    7. Andolfi, Laura & Lima Baima, Renan & Burcheri, Lorenzo Matthias & Pavić, Ivan & Fridgen, Gilbert, 2025. "Sociotechnical design of building energy management systems in the public sector: Five design principles," Applied Energy, Elsevier, vol. 377(PD).
    8. Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    9. Chen, Zhe & Xing, Tian & Wang, Yu & Zhuang, Yunlin & Zheng, Meng & Zhao, Qianchuan & Jia, Qing-Shan, 2025. "Coupling time-scale reinforcement learning methods for building operational optimization with waste heat," Applied Energy, Elsevier, vol. 391(C).
    10. Yuan, Hongping & Zhang, Mengjie & Wang, Zeyu, 2025. "Unveiling the impact of base model selection in heterogeneous ensemble learning for building energy prediction," Energy, Elsevier, vol. 332(C).
    11. Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Maher Abuhussain & Yakubu Aminu Dodo & Saleh Alhumaid & Ali Hussain Alhamami, 2025. "Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities," Energies, MDPI, vol. 18(6), pages 1-22, March.
    12. Alghassab, Mohammed A., 2025. "Data-driven framework for energy optimizing net-zero energy buildings (NZEB): A functional assessment of energy efficiency and thermal comfort," Energy, Elsevier, vol. 330(C).
    13. Sheng Hu & Gongjin Yuan & Kaifeng Hu & Cong Liu & Minghu Wu, 2023. "Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF," Energies, MDPI, vol. 16(12), pages 1-14, June.
    14. Qiu, Dawei & Xue, Juxing & Zhang, Tingqi & Wang, Jianhong & Sun, Mingyang, 2023. "Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading," Applied Energy, Elsevier, vol. 333(C).
    15. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    16. Sun, Yong & Liu, Baoyin & Sun, Zhongrui & Yang, Ruijia, 2023. "Inter-regional cooperation in the transfers of energy-intensive industry: An evolutionary game approach," Energy, Elsevier, vol. 282(C).
    17. 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).
    18. Hanna Jędrzejuk & Dorota Chwieduk, 2021. "Possibilities of Upgrading Warsaw Existing Residential Area to Status of Positive Energy Districts," Energies, MDPI, vol. 14(18), pages 1-17, September.
    19. 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.
    20. Yin, Linfei & Xiong, Yi, 2024. "Incremental learning user profile and deep reinforcement learning for managing building energy in heating water," Energy, Elsevier, vol. 313(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:rensus:v:229:y:2026:i:c:s1364032125012791. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.