Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling
DOI:
https://doi.org/10.33050/itee.v2i2.595Keywords:
Renewable Energy, Machine Learning, Sustainable Waste ManagementAbstract
This research explores the application of Machine Learning techniques in utilizing renewable energy for the recycling process. As the world strives for sustainable solutions to meet energy needs and waste management challenges, this study investigates the integration of Machine Learning algorithms to optimize the production of renewable energy from waste recycling. By employing these algorithms, the research aims to enhance the efficiency and effectiveness of renewable energy generation while promoting environmentally responsible waste management practices. The study encompasses comprehensive data analysis from various recycling facilities, identifying energy consumption patterns and evaluating energy-saving opportunities. The findings reveal that applying Machine Learning can reduce energy consumption by up to 30%, increase recycling output, and decrease greenhouse gas emissions. These results highlight the potential benefits and challenges of implementing smart technology in the recycling process for renewable energy production. Furthermore, the research offers insights into how integrating Machine Learning can support long-term sustainability and significantly contribute to improved environmental management. Consequently, this study paves the way for a cleaner and more sustainable future, inspiring the broader adoption of innovative techniques within the waste management and renewable energy industries.
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