Thermodynamics and Energy Systems: Machine Learning for Energy Optimization
machine learning; energy optimization; thermodynamic systems; integration
This paper titled “Thermodynamics and Energy Systems: Machine Learning for Energy Optimization,” is written to integrate the existing research on the crossroads of thermodynamic principles and machine learning techniques. It delivers a comprehensive overview of how machine learning can be applied to traditional thermodynamic frameworks to improve energy optimization across various systems.
The analysis brings out the main points and trends in the application of machine learning for energy optimization, including the potential of predictive analysis to forecast energy demands, the function of real-time data in improving system reactivity, and the issues associated with implementing machine learning algorithms within established thermodynamic systems.
The findings point towards a significant promise in applying machine learning to energy optimization. These findings are backed by an increasing number of successful case studies and proof-of-concept implementations. However, obstacles such as data quality, integration complexities, and the need for interdisciplinary alliance between thermodynamics and machine learning experts are also highlighted.
This analysis contributes to the ongoing discussion on sustainable energy practices by providing valuable insights for researchers, practitioners, and policymakers engaged in utilizing machine learning to optimize energy systems. The outcomes can inform future research directions and guide the growth of more efficient, data-driven energy management strategies.
"Thermodynamics and Energy Systems: Machine Learning for Energy Optimization", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.b269-b271, October-2024, Available :https://ijnrd.org/papers/IJNRD2410133.pdf
Volume 9
Issue 10,
October-2024
Pages : b269-b271
Paper Reg. ID: IJNRD_301090
Published Paper Id: IJNRD2410133
Downloads: 00030
Research Area: Science and Technology
Country: Shah Alam, SELANGOR , Malaysia
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
