The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Cloud Computing and Machine Learning in the Green Power Sector: Harnessing Sustainable Innovations:
|
|
Author(s): Anurag Vijay Agrawal (Department of Electronics and Communication Engineering, J. P. Institute of Engineering and Technology, India), G. Sujatha (Department of Electronics and Communication Engineering, Sri Venkateswara College of engineering (Autonomous), India), P. Sasireka (Department of Electronics and Communication Engineering, S.A. Engineering College, India), P. Ranjith (Department of Science and Humanity, Sri Sai Ram Institute of Technology, India), S. Cloudin (Department of Computer Science and Engineering, KCG College of Technology, India)and B. Samp (Narasu's Sarathy Institute of Technology, India)
Copyright: 2024
Pages: 29
Source title:
Advanced Applications in Osmotic Computing
Source Author(s)/Editor(s): G. Revathy (SASTRA University, India)
DOI: 10.4018/979-8-3693-1694-8.ch009
Purchase
|
Abstract
The chapter explores the potential of cloud computing, machine learning, and the green power sector in promoting sustainable energy production and consumption. Cloud computing offers efficient data storage and processing, while machine learning algorithms optimize energy production, distribution, and consumption. It highlights how cloud-based infrastructure can enhance renewable energy forecasting, energy grid management, and demand response systems. Edge computing brings intelligence closer to renewable energy sources, reducing latency and energy consumption. The chapter also addresses challenges like data privacy, security, and regulatory compliance in the green power sector. It reviews case studies and emerging trends to demonstrate how these technologies can optimize renewable energy production and contribute to a more sustainable power sector.
Related Content
|
Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid.
© 2026.
32 pages.
|
|
Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid.
© 2026.
32 pages.
|
|
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi.
© 2026.
32 pages.
|
|
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi.
© 2026.
36 pages.
|
|
Salaheldin Mohamed Ibrahim Edam.
© 2026.
42 pages.
|
|
Rubi Kadyan, Sunita Rani, Vinod Kr. Saroha.
© 2026.
46 pages.
|
|
Mamoon M. Saeed, Zeinab E. Ahmed, Rania A. Mokhtar, Rashid A. Saeed.
© 2026.
34 pages.
|
|
|