Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Optimizing Power Consumption in Different Climate Zones Through Smart Energy Management: A Smart Grid Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

According to consumer demand, power demand will sharply rise in the future. This need for power is essential for the growth of our country. Therefore, managing energy is crucial to achieve consumer satisfaction and a high standard of life. The traditional grid has been transformed into a smart grid for smart energy management through the integration of renewable energy sources, communication infrastructure, and intelligent automation. The largest electricity users are buildings and other structures. In order to use resources effectively, there is a need to limit energy usage. In order to analyse the decrease in power consumption of consumer appliances by adding automation and renewable energy, an energy management model has been developed. This Model works on a strategy which is based on load control, temperature and weather control, and occupancy control, as well as national grid power savings through renewable energy. This aids in the effective management of energy production and consumption. The proposed system includes ANN system for synchronization between power supplied by grid to appliances and power produced by green energy. These reduce the load demand from utility grid and electricity bill cost.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this published article.

Abbreviations

RES:

Renewable energy source

HVAC:

Heating, ventilation and air conditioning

ANN:

Artificial neural network

SM:

Smart grid

IoT:

Internet of things

KWh:

Kilowatt hour

EMS:

Energy management system

DSM:

Demand side management

EER:

Energy efficiency ratio

UG:

Utility grid

References

  1. U.S. Department of Energy. (2021). Building Technologies Office: Energy Efficient Buildings. Washington, https://www.energy.gov/eere/buildings/energy-efficient-buildings

  2. U.S. Department of Energy. (2019). Heating and cooling. https://www.energy.gov/energysaver/heating-and-cooling

  3. Singh, K., Pandey, R. K., & Kumar, A. (2017). Integration of HVAC systems with smart grid for energy management. In: 2017 IEEE 7th International Conference on Power Systems (ICPS), 2017, pp. 1–6.

  4. Härkönen, K., Hannola, L. & Pyrhönen, O. (2022). Advancing the smart city objectives of electric demand management and new services to residents by home automation—learnings from a case. Energy Efficiency, 15(25). https://doi.org/10.1007/s12053-022-10032-1

  5. Ahuja, D., & Tatsutani, M. (2009). Sustainable energy for developing countries, S.A.P.I.EN.S. http://journals.openedition.org/sapiens/823.

  6. Islam, M.A., Hasanuzzaman, M., Rahim, N.A., Nahar, A., & Hosenuzzaman, M. (2014). Global renewable energy-based electricity generation and smart grid system for energy security. The Scientific World Journal, 2014 (13). https://doi.org/10.1155/2014/197136.

  7. Archana, R. S., & Singh, S. (2022). Development of smart grid for the power sector in India. Cleaner Energy Systems. https://doi.org/10.1016/j.cles.2022.100011

    Article  Google Scholar 

  8. Gul, M. S. & Patidar, S. (2015). Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy and Buildings, 87 (155–165). https://doi.org/10.1016/j.enbuild.2014.11.027

  9. Yuan, C., Wang, Y., Zhang, C., & Zhao, Y. (2020). A review on artificial intelligence applications in energy systems. Applied Energy, 279, 115739. https://doi.org/10.1016/j.apenergy.2020.115739

  10. Kaneko, Y., Matsushita, M., Kitagami, S., & Kiyohara, R. (2013). An energy-saving office lighting control system linked to employee’s entry/exist. In: Proceedings of the IEEE 2nd Global Conference on Consumer Electronics (GCCE), Tokyo, Japan

  11. Tiller, D. K., Guo, X., Henze, G. P., & Waters, C. E. (2010). Validating the application of occupancy sensor networks for lighting control. Lighting Research Technology., 42(4), 399–414. https://doi.org/10.1177/1477153510375524

    Article  Google Scholar 

  12. Cheng, Z., Duan, J., & Chow, M.-Y. (2018). To centralize or to distribute: that is the question: a comparison of advanced microgrid management systems. IEEE Industrial Electronics Magazine. https://doi.org/10.1109/MIE.2018.2789926.

  13. Hafeez, G., & Alimgeer, K. S. et al. (2020). An innovative optimization strategy for efficient energy management with day-ahead demand response signal and energy consumption forecasting in smart grid using artificial neural network. IEEE Access, 8, 84415–84433.

    Article  Google Scholar 

  14. Tientcheu, N., Simplice, I., Chowdhury, S. P., & Olwal, T. O. (2019). Intelligent energy management strategy for automated office buildings. Energies, 12(22), 4326.

    Article  Google Scholar 

  15. Wang, Y., Lin, X., & Pedram, M. (2014). Adaptive control for energy storage systems in households with photovoltaic modules. IEEE Transactions on Smart Grid, 5(2), 992–1001.

    Article  Google Scholar 

  16. Yao, L., Lai, C. C., & Lim, W. H. (2015). Home energy management system based on photovoltaic system. In: Proceedings of IEEE International Conference on Data Science and Data Intensive Systems

  17. Jafari, M., Malekjamshidi, Z., Zhu, J., & Khooban, M. -H. (2020). A novel predictive fuzzy logic-based energy management system for grid-connected and off-grid operation of residential smart micro grids. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8, 1391–1404.

  18. Zhang, W., Lee, F. C., & Huang, P. Y. (2014). Energy management system control and experiment for future home. IEEE Energy Conversion Congress and Exposition, 4, 3317–3324.

    Google Scholar 

  19. Zhao, P., Suryanarayanan, S., & Simoes, M. G. (2013). An energy management system for building structures using a multi-agent decision-making control methodology. IEEE Transactions on Industry Applications, 49(1), 322–330.

  20. Rehman, A. U., et al. (2021). An efficient energy management in smart grid considering demand response program and renewable energy sources. IEEE Access, 9, 148821–148844.

    Article  Google Scholar 

  21. Soni, P., & Subhashini, J. (2021). Development of an efficient energy management strategy to reduce energy consumption of office building equipment. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-09336-9

    Article  Google Scholar 

  22. Soni, P., & Subhashini, J. (2023). Artificial neural network-based development of an efficient energy management strategy for office building. Intelligent Automation & Soft Computing, 37(1), 1225–1242. https://doi.org/10.32604/iasc.2023.038155

  23. Weatherspark. (2022). Fromhttps://weatherspark.com/y/108259/Average-Weather-in-Indore-India-Year-Round

  24. NREL, Solar resources dataset. National laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Operated by the Alliance for Sustainable Energy, LLC9, 2022. [Online]. Available: https://pvwatts.nrel.gov/pvwatts.php.

  25. Lienhard, J.H. (2001). The general problem of heat transfert. In: A Heat Transfer Textbook (3rd, pp. 7–8). Available online: http://www.mie.uth.gr/labs/ltte/grk/pubs/ahtt.pdf]

  26. Nave, R. (2017). Hyperphysics. Fromhttp://hyperphysics.phy-str.gsu.edu/hbase/hframe.html

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, SRM University, Kattankulathur, Chengalpattu Dt., Tamilnadu, 603203, India

    Payal Soni & J. Subhashini

Authors
  1. Payal Soni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Subhashini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Subhashini.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

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

Soni, P., Subhashini, J. Optimizing Power Consumption in Different Climate Zones Through Smart Energy Management: A Smart Grid Approach. Wireless Pers Commun 131, 2969–2990 (2023). https://doi.org/10.1007/s11277-023-10591-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10591-1

Keywords

Search

Navigation