Skip to main content
Have a personal or library account? Click to login
Analyzing how MIS can optimize the distribution of energy in smart grids, focusing on data-driven decision-making processes Cover

Analyzing how MIS can optimize the distribution of energy in smart grids, focusing on data-driven decision-making processes

Open Access
|Jun 2025

References

  1. Akhavan-Hejazi, H., & Mohsenian-Rad, H. (2018). Power systems big data analytics: An assessment of paradigm shift barriers and prospects. Energy Reports, 4, 91-100.
  2. Akindote, O. J., Egieya, Z. E., Ewuga, S. K., Omotosho, A., & Adegbite, A. O. (2023). A review of data-driven business optimization strategies in the US economy. International Journal of Management & Entrepreneurship Research, 5(12), 1124-1138.
  3. Al Shobaki, M. J., & Naser, S. S. A. (2016). Performance development and its relationship to demographic variables among users of computerized management information systems in Gaza electricity Distribution Company.
  4. Altındal, Ş., Karadeniz, S., Tuğluoğlu, N., & Tataroğlu, A. D. E. M. (2003). The role of interface states and series resistance on the I–V and C–V characteristics in Al/SnO2/p-Si Schottky diodes. Solid-State Electronics, 47(10), 1847-1854.
  5. Avancini, D. B., Rodrigues, J. J., Martins, S. G., Rabêlo, R. A., Al-Muhtadi, J., & Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal of cleaner production, 217, 702-715.
  6. Colmenares-Quintero, R. F., Quiroga-Parra, D. J., Rojas, N., Stansfield, K. E., & Colmenares-Quintero, J. C. (2021). Big Data analytics in Smart Grids for renewable energy networks: Systematic review of information and communication technology tools. Cogent Engineering, 8(1), 1935410.
  7. Dandl, F., Engelhardt, R., Hyland, M., Tilg, G., Bogenberger, K., & Mahmassani, H. S. (2021). Regulating mobility-on-demand services: Tri-level model and bayesian optimization solution approach. Transportation Research Part C: Emerging Technologies, 125, 103075.
  8. Green, M. A., King, F. D., & Shewchun, J. (1974). Minority carrier MIS tunnel diodes and their application to electron-and photo-voltaic energy conversion—I. Theory. Solid-State Electronics, 17(6), 551-561.
  9. Husin, H., & Zaki, M. (2021). A critical review of the integration of renewable energy sources with various technologies. Protection and control of modern power systems, 6(1), 1-18.
  10. Iyer, S. K., & Kumar, H. (2024). Integrating Big Data Technologies for Sustainable Urban Development: Analyzing the Impact of Data-Driven Decision-Making on Green Infrastructure and Smart City Initiatives. International Journal of Social Analytics, 9(11), 1-12.
  11. J. Heaton, An empirical analysis of feature engineering for predictive modeling, in Proc. SoutheastCon, Mar. 2016, pp. 16.
  12. J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, Feature selection: A data perspective, ACM Comput. Surv., vol. 50, no. 6, p. 94, 2016
  13. J. Li and M. Hu, Continuous model adaptation using online meta learning for smart grid application, IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 8, pp. 36333642, Aug. 2021.
  14. J. Y. Kim, Y. M. Hwang, Y. G. Sun, I. Sim, D. I. Kim, and X. Wang, Detection for non-technical loss by smart energy theft with intermediate monitor meter in smart grid, IEEE Access, vol. 7, pp. 129043129053, 2019.
  15. Jadhav, A. M., & Patne, N. R. (2017). Priority-based energy scheduling in a smart distributed network with multiple microgrids. IEEE Transactions on Industrial Informatics, 13(6), 3134-3143.
  16. Jadhav, A. M., & Patne, N. R. (2017). Priority-based energy scheduling in a smart distributed network with multiple microgrids. IEEE Transactions on Industrial Informatics, 13(6), 3134-3143.
  17. Kang, C., Kirschen, D., & Green, T. C. (2023). The evolution of smart grids. Proceedings of the IEEE, 111(7), 691-693.
  18. Karadeniz, S., Tuğluoğlu, N., Serin, T., & Serin, N. (2005). The energy distribution of the interface state density of SnO2/p-Si (1 1 1) heterojunctions prepared at different substrate temperatures by spray deposition method. Applied surface science, 246(1-3), 30-35.
  19. Ketter, W., Collins, J., Saar-Tsechansky, M., & Marom, O. (2018). Information systems for a smart electricity grid: Emerging challenges and opportunities. ACM Transactions on Management Information Systems (TMIS), 9(3), 1-22.
  20. L. A. Passos, C. C. O. Ramos, D. Rodrigues, D. R. Pereira, A. N. de Souza, K. A. P. da Costa, and J. P. Papa, Unsupervised non-technical losses identi cation through optimum-path forest, Electr. Power Syst. Res., vol. 140, pp. 413423, Nov. 2016.
  21. L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar, Adversarial machine learning, inProc.4thACMWorkshopSecur.Artif. Intell., 2011, pp. 4358.
  22. L. Xie, Y. Mo, and B. Sinopoli, False data injection attacks in electricity markets, in Proc. 1st IEEE Int. Conf. Smart Grid Commun., Oct. 2010, pp. 226231.
  23. L.-Y. Lu, H. J. Liu, H. Zhu, and C.-C. Chu, Intrusion detection in distributed frequency control of isolated microgrids, IEEE Trans. Smart Grid, vol. 10, no. 6, pp. 65026515, Nov. 2019.
  24. Lévy, L. N. (2024). Advanced clustering and AI-driven decision support systems for smart energy management (Doctoral dissertation, Université Paris-Saclay).
  25. Li, J., Dai, J., Issakhov, A., Almojil, S. F., & Souri, A. (2021). Towards decision support systems for energy management in the smart industry and Internet of Things. Computers & Industrial Engineering, 161, 107671.
  26. Loock, C. M., Staake, T., & Thiesse, F. (2013). Motivating energy-efficient behavior with green IS: an investigation of goal setting and the role of defaults. MIS quarterly, 1313-1332.
  27. M. Li, K. Zhang, J. Liu, H. Gong, and Z. Zhang, Blockchain-based anomaly detection of electricity consumption in smart grids, Pattern Recognit. Lett., vol. 138, pp. 476482, Oct. 2020.
  28. Nadakuditi, S., Agrawal, S., & Kumar, B. (2021). Data Analytics and Business Analysis: How Business Analysts Can Drive Data-Driven Decision-Making in Organizations. European Journal of Advances in Engineering and Technology, 8(1), 71-75.
  29. Naser, S. S. A., & Al Shobaki, M. J. (2016). Computerized Management Information Systems Resources and their Relationship to the Development of Performance in the Electricity Distribution Company in Gaza.
  30. Ning, K. (2021). Data driven artificial intelligence techniques in renewable energy system (Doctoral dissertation, Massachusetts Institute of Technology).
  31. P. D. Diamantoulakis, V. M. Kapinas, and G. K. Karagiannidis, Big data analytics for dynamic energy management in smart grids, Big Data Res., vol. 2, pp. 94101, Sep. 2015.
  32. P. Engebretson, The Basics of Hacking and Penetration Testing: Ethical Hacking and Penetration Testing Made Easy. Amsterdam, The Netherlands: Elsevier, 2013.
  33. Pakma, O., Serin, N., Serin, T., & Altındal, Ş. (2011). On the energy distribution profile of interface states obtained by taking into account of series resistance in AI/TiO2/p–Si (MIS) structures. Physica B: Condensed Matter, 406(4), 771-776.
  34. R. Razavi, A. Gharipour, M. Fleury, and I. J. Akpan, A practical feature engineering framework for electricity theft detection in smart grids, Appl. Energy, vol. 238, pp. 481494, Mar. 2019.
  35. S. Afrin and S. Mishra, An anonymized authentication framework for smart metering data privacy, in Proc. IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf. (ISGT), Sep. 2016, pp. 15.
  36. S. Basumallik, S. Eftekharnejad, N. Davis, and B. K. Johnson, Impact of false data injection attacks on PMU-based state estimation, in Proc. North Amer. Power Symp. (NAPS), Sep. 2017, pp. 16.
  37. S. K. Singh, K. Khanna, R. Bose, B. K. Panigrahi, and A. Joshi, Joint transformation-based detection of false data injection attacks in smart grid, IEEE Trans. Ind. Informat., vol. 14, no. 1, pp. 8997, Jan. 2017.
  38. S. Pal, B. Sikdar, and J. H. Chow, Classi cation and detection of PMU data manipulation attacks using transmission line parameters, IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 50575066, Sep. 2017.
  39. S. Salinas, M. Li, and P. Li, Privacy-preserving energy theft detection in smart grids, in Proc. 9th Annu. IEEE Commun. Soc. Conf. Sensor, Mesh Ad Hoc Commun. Netw. (SECON), Jun. 2012, pp. 605613.
  40. Shahat Osman, A. M., & Elragal, A. (2021). Smart cities and big data analytics: a data-driven decision-making use case. Smart Cities, 4(1), 286-313.
  41. Shahbaz, M., Raghutla, C., Song, M., Zameer, H., & Jiao, Z. (2020). Public-private partnerships investment in energy as new determinant of CO2 emissions: the role of technological innovations in China. Energy Economics, 86, 104664.
  42. Sievers, J., & Blank, T. (2023). A systematic literature review on data driven residential and industrial energy management systems. Energies, 16(4), 1688.
  43. T. Alladi, V. Chamola, J. J. P. C. Rodrigues, and S. A. Kozlov, Blockchain in smart grids: A review on different use cases, Sensors, vol. 19, no. 22, p. 4862, Nov. 2019.
  44. U. Khurana, H. Samulowitz, and D. Turaga, Feature engineering for predictive modeling using reinforcement learning, in Proc. 32nd AAAI Conf. Artif. Intell., 2018, pp. 18.
  45. W.-L. Chin, Y.-H. Lin, and H.-H. Chen, A framework of machine to-machine authentication in smart grid: A two-layer approach, IEEE Commun. Mag., vol. 54, no. 12, pp. 102107, Dec. 2016.
  46. W. Li, T. Logenthiran, V.-T. Phan, and W. L. Woo, A novel smart energy theft system (SETS) for IoT-based smart home, IEEE Internet Things J., vol. 6, no. 3, pp. 55315539, Jun. 2019.
  47. Wang, D., Liu, W., Liang, Y., & Wei, S. (2023). Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior. Annals of Operations Research, 1-22.
  48. Weitemeyer, S., Kleinhans, D., Vogt, T., & Agert, C. (2015). Integration of Renewable Energy Sources in future power systems: The role of storage. Renewable Energy, 75, 14-20.
  49. X. Yuan, M.-G. Shi, and Z. Sun, Research of electricity stealing ident cation method for distributed PV based on the least squares approach, in Proc. 5th Int. Conf. Electr. Utility Deregulation Restructuring Power Technol. (DRPT), Nov. 2015, pp. 24712474.
  50. Y. Chen, Y. Tan, and D. Deka, Is machine learning in power systems vulnerable? in Proc. IEEE Int. Conf. Commun., Control, Comput. Technol. Smart Grids (Smart Grid Comm), Oct. 2018, pp. 16.
  51. Z. El Mrabet, N. Kaabouch, H. El Ghazi, and H. El Ghazi, Cyber security in smart grid: Survey and challenges, Comput. Elect. Eng., vol. 67, pp. 469482, Apr. 2018.
  52. Z. Guan, G. Si, X. Zhang, L. Wu, N. Guizani, X. Du, and Y. Ma, Privacy preserving and ef cient aggregation based on blockchain for power grid communications in smart communities, IEEE Commun. Mag., vol. 56, no. 7, pp. 8288, Jul. 2018.
  53. Zainab, A., Ghrayeb, A., Syed, D., Abu-Rub, H., Refaat, S. S., & Bouhali, O. (2021). Big data management in smart grids: Technologies and challenges. IEEE Access, 9, 73046-73059.
Language: English
Page range: 31 - 42
Submitted on: Feb 24, 2025
Accepted on: Jun 15, 2025
Published on: Jun 15, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Answer Hossain, Dorcas Oyebode, Kazi Abdullah Al Imon, Shah Md. Wasif Faisal, Rajesh Vayyala, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.