Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.


  • Ahmad Y. A. Bani Ahmad Department of Accounting and Finance Science, Faculty of Business, Middle East University, Amman 11831, Jordan, 8788
  • P. William Dean, Research and Development, Department of Information Technology, Sanjivani College of Engineering, Savitribai Phule Pune University, Pune
  • Dipesh Uike Professor, Department of MBA, Dr. Ambedkar Institute of Management Studies and Research, Nagpur, Maharashtra
  • Amol Murgai Associate Professor in the School of Business and Management, Christ University, Lavasa, Pune
  • K. K. Bajaj RNB, Global University, Bikaner, Rajasthan
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu


Smart Grid, Machine Learning (ML), Internet of Things (IOT), Energy Management Model (EMM)


Maintaining a consistent supply of power is essential for the well-being of the economy, the public, and one's own health. The generation of energy, as well as its distribution, monitoring, and management, are all undergoing fundamental changes as a result of the implementation of a smart grid (SG), which is authorised to include communication technology and sensors into power systems. There are a lot of problems that need to be fixed before the interoperability of the smart grid can be determined. The integration of renewable energy sources and smart grid technology market size and energy management is a sustainable solution to the problem of energy demand management. The importance work quickly toward the development of an efficient Energy Management Model (EMM) that integrates smart grids and renewable energy sources. When it comes to the modelling of complex and non-linear data, machine learning (ML), Internet of Things (IoT) approaches often perform better than statistical models. So, utilizing a machine learning approach for the EMM is a good option since it simplifies the EMM by generating a single trained model to anticipate its performance characteristics across all conditions. This may be accomplished via the use of an EMM created using an ML method. It was recommended that a certain flexibility sample be used as a control mechanism for incursion into the smart grid. The outcomes of the experiment indicate that the demand-side management (DSM) device is more resistant to infiltration and is enough to lower the energy usage of the smart grid.


Download data is not yet available.


Lee, J.; Yoon, S.; Hwang, E. Frequency Selective Auto-Encoder for Smart Meter Data Compression. Sensors 2021,21, 1521. [CrossRef]

Diamantoulakis, P.; Dalamagkas, C.; Radoglou-Grammatikis, P.; Sarigiannidis, P.; Karagiannidis, G. Game-theoretic honeypot deployment in smart Grid. Sensors 2020, 20, 4199.

Khajenasiri, I.; Estebsari, A.; Verhelst, M.; Gielen, G. A review on Internet of Things solutions for intelligent energy control in buildings for smart city applications. Energy Procedia 2017, 111, 770–779.

Machorro-Cano, I.; Alor-Hernández, G.; Paredes-Valverde, M.A.; Rodríguez-Mazahua, L.; Sánchez-Cervantes, J.L.; OlmedoAguirre, J.O. HEMS-IoT: A big data and machine learning-based smart home system for energy saving. Energies 2020,13, 1097.

Mohanta, B.K.; Jena, D.; Satapathy, U.; Patnaik, S. Survey on IoT security: Challenges and solution using machine learning, artificial intelligence, and blockchain technology. Internet Things 2020, 11, 100227.

Yadav, Dharmendra, Anjali R. Mahajan, and A. Thomas. "Security risk analysis approach for smart grid." International Journal of Smart Grid and Green Communications 1.3 (2018): 206- 215.

Weerakkody, Sean, and Bruno Sinopoli. "Challenges and opportunities: Cyber-physical security in the smart grid." Smart Grid Control. Springer, Cham, 2019. 257-273.

Alladi, Tejasvi, et al. "Blockchain in smart grids: A review on different use cases." Sensors 19.22 (2019): 4862.

Narayanan, Sandeep Nair, et al. "Security in smart cyber-physical systems: a case study on smart grids and smart cars." Smart Cities Cybersecurity and Privacy. Elsevier, 2019. 147-163.

Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.

William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore.

K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.

Kumar, A., More, C., Shinde, N. K., Muralidhar, N. V., Shrivastava, A., Reddy, C. V. K., & William, P. (2023). Distributed Electromagnetic Radiation Based Renewable Energy Assessment Using Novel Ensembling Approach. Journal of Nano-and Electronic Physics, 15(4).

William, P., Shrivastava, A., Shunmuga Karpagam, N., Mohanaprakash, T.A., Tongkachok, K., Kumar, K. (2023). Crime Analysis Using Computer Vision Approach with Machine Learning. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore.

Lim, Jiyoung, Inshil Doh, and Kijoon Chae. "Secure and structured IoT smart grid system management." International Journal of Web and Grid Services 13.2 (2017): 170-185.

Sha, Kewei, Naif Alatrash, and Zhiwei Wang. "A secure and efficient framework to read isolated smart grid devices." IEEE Transactions on Smart Grid 8.6 (2016): 2519-2531.

Jean-Paul A. Yaacoub, Ola Salman, Hassan N. Noura, Nesrine Kaaniche, Ali Chehab, Mohamad Malli, Cyber-physical systems security: limitations, issues and future trends, Microprocess. Microsyst. 77 (2020), 103201. ISSN 0141- 9331, http://www.sciencedirect. com/science/article/pii/S0141933120303689.

Hao Piao, Hanming Duan, Miaomiao Zhu, Simulation of urban landscape around subway station based on machine learning and virtual reality, Microprocess. Microsyst. (2020), 103495.

Sadia Din, et al., Constrained application for mobility management using embedded devices in the Internet of Things based urban planning in smart cities, Sustain. Cities Soc. 44 (2019) 144–151.

Auma, G., Levi, S., Santos, M., Ji-hoon, P., & Tanaka, A. Predicting Stock Market Trends using Long Short-Term Memory Networks. Kuwait Journal of Machine Learning, 1(3). Retrieved from

Shalini, S., Srinivasan, S. ., Bansal, N. ., & Prakash, P. . (2023). Developing the Computational Building Blocks for General Intelligent in SOAR. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 57–63.




How to Cite

Bani Ahmad, A. Y. A. ., William, P. ., Uike, D. ., Murgai, A. ., Bajaj, K. K. ., Deepak, A. ., & Shrivastava, A. . (2023). Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 581–590. Retrieved from



Research Article

Most read articles by the same author(s)

1 2 3 4 5 > >>