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Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights

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Data Analytics for Smart Grids Applications—A Key to Smart City Development

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 247))

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Abstract

In recent years, there has been a growing trend towards integrating data analytics into smart grids as a method of providing increased performance, optimised energy use, and deeper insights into network operations. The term “data analytics” refers to the process of collecting and analysing data from a wide variety of sources, including “smart metres,” “distribution grids,” and “industrial plants.” This information is then put to use in the development of models and algorithms that can assist in recognising and predicting patterns and trends in energy consumption. This may help to improve customer service while also lowering operational expenses and optimising energy efficiency. In addition, data analytics can be used to detect and prevent energy theft, minimise energy waste, detect system failures, and indicate areas in which improvements could be made. Smart grids have the potential to become energy networks that are more intelligent, efficient, and reliable if data analytics are utilised.

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References

  1. Munshi, A., Mohamed, Y.A.-R.I.: Data lake lambda architecture for smart grids big data analytics. IEEE Access 6, 40463–40471 (2018). https://doi.org/10.1109/ACCESS.2018.2858256

    Article  Google Scholar 

  2. Syed, D., Zainab, A., Ghrayeb, A., Refaat, S.S., Abu-Rub, H., Bouhali, O.: Smart grid big data analytics: survey of technologies, techniques, and applications. IEEE Access 9, 59564–59585 (2021). https://doi.org/10.1109/ACCESS.2020.3041178

    Article  Google Scholar 

  3. Wang, Y., Chen, Q., Hong, T., Kang, C.: Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans. Smart Grid 10(3), 3125–3148 (2019). https://doi.org/10.1109/TSG.2018.2818167

    Article  Google Scholar 

  4. Sun, L., Zhou, K., Zhang, X., Yang, S.: Outlier data treatment methods toward smart grid applications. IEEE Access 6, 39849–39859 (2018). https://doi.org/10.1109/ACCESS.2018.2852759

    Article  Google Scholar 

  5. Khan, M.A., Siddiqui, M.S., Rahmani, M.K.I., Husain, S.: Investigation of big data analytics for sustainable smart city development: an emerging country. IEEE Access 10, 16028–16036 (2022). https://doi.org/10.1109/ACCESS.2021.3115987

    Article  Google Scholar 

  6. Chen, K., He, Z., Wang, S.X., Hu, J., Li, L., He, J.: Learning-based data analytics: moving towards transparent power grids. CSEE J. Power Energy Syst. 4(1), 67–82 (2018). https://doi.org/10.17775/CSEEJPES.2017.01070

    Article  Google Scholar 

  7. Wang, Y., Amin, M.M., Fu, J., Moussa, H.B.: A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids. IEEE Access 5, 26022–26033 (2017). https://doi.org/10.1109/ACCESS.2017.2769099

    Article  Google Scholar 

  8. Ghorbanian, M., Dolatabadi, S.H., Siano, P.: Big data issues in smart grids: a survey. IEEE Syst. J. 13(4), 4158–4168 (2019). https://doi.org/10.1109/JSYST.2019.2931879

    Article  Google Scholar 

  9. Rossi, Chren, S.: Smart grids data analysis: a systematic mapping study. IEEE Trans. Indus. Inform. 16(6), 3619–3639 (2020). https://doi.org/10.1109/TII.2019.2954098

  10. Guan, Z., Zhou, X., Liu, P., Wu, L., Yang, W.: A blockchain-based dual-side privacy-preserving multiparty computation scheme for edge-enabled smart grid. IEEE Internet Things J. 9(16), 14287–14299 (2022). https://doi.org/10.1109/JIOT.2021.3061107

  11. Priyadarshini, I., Alkhayyat, A., Obaid, A.J., Sharma, R.: Water pollution reduction for sustainable urban development using machine learning techniques. Cities 130, 103970 (2022). ISSN 0264-2751. https://doi.org/10.1016/j.cities.2022.103970

  12. Pandya, S., Gadekallu, T.R., Maddikunta, P.K.R., Sharma, R.: A study of the impacts of air pollution on the agricultural community and yield crops (Indian context). Sustainability 14, 13098 (2022). https://doi.org/10.3390/su142013098

    Article  Google Scholar 

  13. Bhola, B., Kumar, R., Rani, P., Sharma, R., Mohammed, M.A., Yadav, K., Alotaibi, S.D., Alkwai, L.M.: Quality-enabled decentralized dynamic IoT platform with scalable resources integration. IET Commun. 00, 1–10 (2022). https://doi.org/10.1049/cmu2.12514

    Article  Google Scholar 

  14. Deepanshi, I.B., Garg, D., Kumar, N., Sharma, R.: A comprehensive review on variants of SARS-CoVs-2: challenges, solutions and open issues. Comput. Commun. (2022). ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2022.10.013

  15. Ahasan Habib, A.K.M., Hasan, M.K., Islam, S., Sharma, R., Hassan, R., Nafi, N., Yadav, K., Alotaibi, S.D.: Energy-efficient system and charge balancing topology for electric vehicle application. Sustain. Energy Technol. Assess. 53(Part B), 102516 (2022). ISSN 2213-1388. https://doi.org/10.1016/j.seta.2022.102516

  16. Rani, P., Sharma, R.: Intelligent transportation system for internet of vehicles based vehicular networks for smart cities. Comput. Electr. Eng. 105, 108543 (2023). ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2022.108543

  17. Sharma, R., Rawat, D.B., Nayak, A., Peng, S.-L., Xin, Q.: Introduction to the special section on survivability analysis of wireless networks with performance evaluation (VSI–networks survivability). Comput. Netw. 220, 109498 (2023). ISSN 1389-1286. https://doi.org/10.1016/j.comnet.2022.109498

  18. Ghildiyal, Y., Singh, R., Alkhayyat, A., Gehlot, A., Malik, P., Sharma, R., Akram, S.V., Alkwai, L.M.: An imperative role of 6G communication with perspective of industry 4.0: challenges and research directions. Sustain. Energy Technol. Assess. 56, 103047 (2023). ISSN 2213-1388. https://doi.org/10.1016/j.seta.2023.103047

  19. Ahasan Habib, A.K.M., Hasan, M.K., Alkhayyat, A., Islam, S., Sharma, R., Alkwai, L.M.: False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction. Comput. Electr. Eng. 107, 108638 (2023). ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2023.108638

  20. Priyadarshini, I., Kumar, R., Alkhayyat, A., Sharma, R., Yadav, K., Alkwai, L.M., Kumar, S.: Survivability of industrial internet of things using machine learning and smart contracts. Comput. Electr. Eng. 107, 108617 (2023). ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2023.108617

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Authors and Affiliations

  1. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

    Praveen Kumar Malik

  2. Scientific Research Centre of the Islamic University, The Islamic University, Najaf, Iraq

    Ahmed Hussein Alkhayyat

Authors
  1. Praveen Kumar Malik
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  2. Ahmed Hussein Alkhayyat
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Correspondence to Praveen Kumar Malik .

Editor information

Editors and Affiliations

  1. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Rohit Sharma

  3. Dept. of Embedded Systems Engineering, Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

  4. Computer Science&Engineering, GIET University, Gunupur, Gunupur, India

    Raghvendra Kumar

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Malik, P.K., Alkhayyat, A.H. (2023). Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights. In: Kumar Sharma, D., Sharma, R., Jeon, G., Kumar, R. (eds) Data Analytics for Smart Grids Applications—A Key to Smart City Development. Intelligent Systems Reference Library, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-031-46092-0_13

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