Skip to main content
SpringerLink
Log in

An intelligent mechanism for energy consumption scheduling in smart buildings

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In recent years, the incorporation of sensing technology into residential buildings has given rise to the concept of ”smart buildings”, aimed at enhancing resident comfort. These buildings are typically part of interconnected neighborhoods sharing common energy sources, which makes the energy consumption a critical consideration in decision-making processes. Consequently, optimizing energy usage in smart buildings has posed significant challenges for both enterprises and governments, prompting numerous studies to address this issue. One such challenge is organizing energy usage within neighborhood networks while ensuring the user comfort and without exceeding the total energy capacity. In this paper, we present a novel mechanism that predicts the future behavior of each house based on its historical consumption data, generating a weekly schedule annotated with hourly energy usage levels (high, normal, or low) tailored to individual user needs. Additionally, we introduce an incentive-based program that rewards users with bill discounts for adhering to high energy consumption periods. The scheduling process involves extracting features from data and utilizing a genetic algorithm for construction, coupled with dynamic programming to enhance efficiency by storing house features and schedules. This enables rapid provision of suitable schedules for similar houses. Evaluation results demonstrate that the proposed technique achieves an accuracy of \(92\%\) and improves the execution time of the optimization algorithm by \(26\%\).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availibility

The dataset used in this work is available online in the following link: .

References

  1. Zaimen, K., Brahmia MeA., et al.: A overview on wsn deployment and a novel conceptual bim-based approach in smart buildings. In: 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp 1–6 (2020). https://doi.org/10.1109/IOTSMS52051.2020.9340226

  2. Farhangi, H.: The path of the smart grid. IEEE Power Energ. Mag. 8(1), 18–28 (2009)

    Article  Google Scholar 

  3. Jin, W., Ullah, I., Ahmad, S., et al.: Occupant comfort management based on energy optimization using an environment prediction model in smart homes. Sustainability 11(4), 997 (2019)

    Article  Google Scholar 

  4. Merdanoğlu, H., Yakıcı, E., Doğan, O.T., et al.: Finding optimal schedules in a home energy management system. Electric Power Syst. Res. 182(106), 229 (2020)

    Google Scholar 

  5. Celik, B., Roche, R., Suryanarayanan, S., et al.: Electric energy management in residential areas through coordination of multiple smart homes. Renew. Sustain. Energy Rev. 80, 260–275 (2017)

    Article  Google Scholar 

  6. Macedo, M., Galo, J., de Almeida, L., et al.: Demand side management using artificial neural networks in a smart grid environment. Renew. Sustain. Energy Rev. 41, 128–133 (2015). https://doi.org/10.1016/j.rser.2014.08.035. (https://www.sciencedirect.com/science/article/pii/S1364032114007114)

    Article  Google Scholar 

  7. Zhou, K., Yang, S.: Demand side management in China: The context of China’s power industry reform. Renew. Sustain. Energy Rev. 47, 954–965 (2015)

    Article  Google Scholar 

  8. Mohajeryami, S., Schwarz, P., Baboli, P.T.: Including the behavioral aspects of customers in demand response model: real time pricing versus peak time rebate. In: 2015 North American Power Symposium (NAPS), IEEE, pp 1–6 (2015)

  9. Albadi, M.H., El-Saadany, E.F.: Demand response in electricity markets: an overview. In: 2007 IEEE power engineering society general meeting, IEEE, pp 1–5 (2007)

  10. Ullah, I., Kim, D.: An improved optimization function for maximizing user comfort with minimum energy consumption in smart homes. Energies 10(11), 1818 (2017)

    Article  Google Scholar 

  11. Ali, S., Kim, D.H.: Effective and comfortable power control model using Kalman filter for building energy management. Wirel. Pers. Commun. 73(4), 1439–1453 (2013)

    Article  Google Scholar 

  12. González-Vidal, A., Ramallo-González, A.P., Terroso-Sáenz, F., et al.: Data driven modeling for energy consumption prediction in smart buildings. In: 2017 IEEE International Conference on Big Data (Big Data), IEEE, pp 4562–4569 (2017)

  13. Wang, Z., Yang, R., Wang, L.: Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. In: IECON 2010-36th annual conference on IEEE industrial electronics society, IEEE, 1144–1149 (2010)

  14. Dimara, A., Vasilopoulos, V.G., Krinidis, S., et al.: Nrg4-u: a novel home energy management system for a unique loadprofile. Energy Sources, Part A: Recovery, Utilization, Environ. Effects 44(1), 353–378 (2022)

    Article  Google Scholar 

  15. Chenguang, L.: An efficient vision-based approach for optimizing energy consumption in internet of things and smart homes. Int. J. Adv. Comput. Sci. Appl. (2023). https://doi.org/10.14569/IJACSA.2023.0140672

    Article  Google Scholar 

  16. Chen, Y.Y., Chen, M.H., Chang, C.M., et al.: A smart home energy management system using two-stage non-intrusive appliance load monitoring over fog-cloud analytics based on Tridium’s Niagara framework for residential demand-side management. Sensors 21(8), 2883 (2021)

    Article  Google Scholar 

  17. Khalil, M., Esseghir, M., Merghem-Boulahia, L.: A federated learning approach for thermal comfort management. Adv. Eng. Inform. 52(101), 526 (2022)

    Google Scholar 

  18. Roy, T., Das, A., Ni, Z.: Optimization in load scheduling of a residential community using dynamic pricing. In: 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, pp 1–5 (2017)

  19. Liu, D., Xu, Y., Wei, Q., et al.: Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming. IEEE/CAA J. Automat. Sin. 5(1), 36–46 (2017)

    Article  Google Scholar 

  20. Polaki, S.K., Reza, M., Roy, D.S.: A genetic algorithm for optimal power scheduling for residential energy management. In: 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), IEEE, pp 2061–2065 (2015)

  21. Borisovsky, P., Eremeev, A., Kallrath, J.: Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic. Int. J. Prod. Res. 58(9), 2677–2695 (2020)

    Article  Google Scholar 

  22. Wang, F., Xiang, B., Li, K., et al.: Smart households’ aggregated capacity forecasting for load aggregators under incentive-based demand response programs. IEEE Trans. Ind. Appl. 56(2), 1086–1097 (2020)

    Article  Google Scholar 

  23. Farmani, F., Parvizimosaed, M., Monsef, H., et al.: A conceptual model of a smart energy management system for a residential building equipped with cchp system. Int. J. Electr. Power Energy Syst. 95, 523–536 (2018)

    Article  Google Scholar 

  24. Shuai, H., He, H.: Online scheduling of a residential microgrid via Monte-Carlo tree search and a learned model. IEEE Trans. Smart Grid 12(2), 1073–1087 (2020)

    Article  Google Scholar 

  25. Sivaranjani, R., Rao, P.M.: Smart energy optimization using new genetic algorithms in smart grids with the integration of renewable energy sources. In: Sustain. Netw. Smart Grid, pp. 121–147. Academic Press, London p (2022)

    Chapter  Google Scholar 

  26. Luo, X., Mahdjoubi, L.: Towards a blockchain and machine learning-based framework for decentralised energy management. Energy Build. 303(113), 757 (2024)

    Google Scholar 

  27. Gutiérrez-Gnecchi, J.A., Molina-Moreno, I., Téllez-Anguiano, A.C., et al.: A var model to forecast electricity consumption in smart metering system using an edge-fog-cloud architecture. Res. Comput. Sci. 150, 1–8 (2021)

    Google Scholar 

  28. Fekri, M.N., Grolinger, K., Mir, S.: Distributed load forecasting using smart meter data: Federated learning with recurrent neural networks. Int. J. Electr. Power Energy Syst. 137(107), 669 (2022)

    Google Scholar 

  29. Fernández, J.D., Menci, S.P., Lee, C.M., et al.: Privacy-preserving federated learning for residential short-term load forecasting. Appl. Energy 326(119), 915 (2022)

    Google Scholar 

  30. Araya, D.B., Grolinger, K., ElYamany, H.F., et al.: Collective contextual anomaly detection framework for smart buildings. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 511–518 (2016)

  31. Araya, D.B., Grolinger, K., ElYamany, H.F., et al.: An ensemble learning framework for anomaly detection in building energy consumption. Energy Build. 144, 191–206 (2017)

    Article  Google Scholar 

  32. Fan, C., Xiao, F., Wang, S.: Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Appl. Energy 127, 1–10 (2014)

    Article  Google Scholar 

  33. Gharsellaoui, S., Mansouri, M., Trabelsi, M., et al.: Fault diagnosis of heating systems using multivariate feature extraction based machine learning classifiers. J. Build. Eng. 30(101), 221 (2020)

    Google Scholar 

  34. Makonin, S.: Hue: the hourly usage of energy dataset for buildings in British Columbia. https://doi.org/10.7910/DVN/N3HGRN (2018)

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Author notes

  1. Hassan Harb, Mohamad Hijazi, Mohamed-El-Amine Brahmia, Ali Kadhum Idrees, Mouhammad AlAkkoumi, Ali Jaber and Abdelhafid Abouaissa have contributed equally to this work.

Authors and Affiliations

  1. College of Engineering and Technology, American University of the Middle East, 54200, Egaila, Kuwait

    Hassan Harb & Mouhammad AlAkkoumi

  2. Faculty of Sciences, Lebanese University, Beirut, 1001, Lebanon

    Mohamad Hijazi & Ali Jaber

  3. CESI, LINEACT UR, 7527, Strasbourg, France

    Mohamed-El-Amine Brahmia

  4. Dept. of Information Networks, College of Information Technology, University of Babylon, Babylon, Iraq

    Ali Kadhum Idrees

  5. Computer Science Department, University of Haute-Alsace, IRIMAS UR 7499, 68100, Mulhouse, France

    Abdelhafid Abouaissa

Authors
  1. Hassan Harb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamad Hijazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed-El-Amine Brahmia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Kadhum Idrees
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mouhammad AlAkkoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ali Jaber
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Abdelhafid Abouaissa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: AJ, AA; Methodology: MH, HH; Formal analysis and investigation: M-E-AB, AKI; Writing—original draft preparation: MH; Writing—review and editing: HH, MA; Resources: MA, M-E-AB; Supervision: HH, AA.

Corresponding author

Correspondence to Hassan Harb.

Ethics declarations

Conflict of interest

The authors have no Conflict of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harb, H., Hijazi, M., Brahmia, MEA. et al. An intelligent mechanism for energy consumption scheduling in smart buildings. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04440-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04440-4

Keywords

Search

Navigation