Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application


  • Bhawani Sankar Panigrahi GITAM University image/svg+xml
  • R. Kishore Kanna Jerusalem College of Engineering
  • Pragyan Paramita Das Silicon Institute of Technology
  • Susanta Kumar Sahoo Indira Gandhi Institute of Technology image/svg+xml
  • Tanusree Dutta Vardhaman College of Engineering image/svg+xml



Cloud computing, Machine Learning, Physical Machine


INTRODUCTION: Cloud computing, a still emerging technology,  allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources.

OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption.

METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment.

RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts.

CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.


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05-06-2024 — Updated on 05-06-2024


How to Cite

Panigrahi BS, Kanna RK, Paramita Das P, Sahoo SK, Dutta T. Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application. EAI Endorsed Trans Energy Web [Internet]. 2024 Jun. 5 [cited 2024 Jul. 13];11. Available from: