Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling

Authors

  • Mateo Fernandez Mfinitee Incorporation
  • Adam Faturahman University of Raharja
  • Nesti Anggraini Santoso University of Raharja

DOI:

https://doi.org/10.33050/itee.v2i2.595

Keywords:

Renewable Energy, Machine Learning, Sustainable Waste Management

Abstract

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.

References

M. A. Al-Sharafi et al., “Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison,” Comput Human Behav, vol. 143, p. 107708, 2023.

A. A. Kutty, G. M. Abdella, M. Kucukvar, N. C. Onat, and M. Bulu, “A system thinking approach for harmonizing smart and sustainable city initiatives with United Nations sustainable development goals,” Sustainable Development, vol. 28, no. 5, pp. 1347–1365, 2020.

U. Rusilowati, N. P. L. Santoso, A. Azmi, S. Maulana, and A. Faturahman, “Analyzing the Financial Implications of Increasing Renewable Energy Penetration in Indonesia’s Power System,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM), IEEE, 2023, pp. 1–4.

M. A. Baloch, I. Ozturk, F. V. Bekun, and D. Khan, “Modeling the dynamic linkage between financial development, energy innovation, and environmental quality: does globalization matter?,” Bus Strategy Environ, vol. 30, no. 1, pp. 176–184, 2021.

K. Nam et al., “A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions,” Appl Energy, vol. 266, p. 114893, 2020.

M. Mohammed et al., “The mediating role of policy-related factors in the relationship between practice of waste generation and sustainable construction waste minimisation: PLS-SEM,” Sustainability, vol. 14, no. 2, p. 656, 2022.

T. G. Hlalele, R. M. Naidoo, J. Zhang, and R. C. Bansal, “Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation,” IEEE Access, vol. 8, pp. 38794–38808, 2020, doi: 10.1109/ACCESS.2020.2975674.

A. Masih, “Machine learning algorithms in air quality modeling,” Global Journal of Environmental Science and Management, vol. 5, no. 4, pp. 515–534, 2019, doi: 10.22034/gjesm.2019.04.10.

E. Dollan, B. D. K. Ramadhan, and N. Abrina, “Assessing the Outcomes of Circular Economy and Waste Management Partnerships between Indonesia and Denmark,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 1, pp. 76–83, 2023, doi: 10.34306/itsdi.v5i1.609.

O. Ogunrinde, E. Shittu, and K. K. Dhanda, “Investing in Renewable Energy: Reconciling Regional Policy With Renewable Energy Growth,” IEEE Engineering Management Review, vol. 46, no. 4, pp. 103–111, 2018, doi: 10.1109/EMR.2018.2880445.

C. Tipantuña and X. Hesselbach, “NFV/SDN Enabled Architecture for Efficient Adaptive Management of Renewable and Non-Renewable Energy,” IEEE Open Journal of the Communications Society, vol. 1, pp. 357–380, 2020, doi: 10.1109/OJCOMS.2020.2984982.

A. Al Hadi, C. A. S. Silva, E. Hossain, and R. Challoo, “Algorithm for Demand Response to Maximize the Penetration of Renewable Energy,” IEEE Access, vol. 8, pp. 55279–55288, 2020, doi: 10.1109/ACCESS.2020.2981877.

Y. Sun, Z. Zhao, M. Yang, D. Jia, W. Pei, and B. Xu, “Overview of energy storage in renewable energy power fluctuation mitigation,” CSEE Journal of Power and Energy Systems, vol. 6, no. 1, pp. 160–173, 2020, doi: 10.17775/CSEEJPES.2019.01950.

C. Byers and A. Botterud, “Additional Capacity Value From Synergy of Variable Renewable Energy and Energy Storage,” IEEE Trans Sustain Energy, vol. 11, no. 2, pp. 1106–1109, 2020, doi: 10.1109/TSTE.2019.2940421.

U. Rahardja, “Risk Assessment, Risk Identification, and Control in The Process Of Steel Smelting Using the Hiradc Method,” APTISI Transactions on Management, vol. 7, no. 3, pp. 261–272, 2023.

Harfizar, E. Martin, M. Abdul Aziz, A. Pujihanarko, and N. R. Pratiwi, “Exploring the Research on Utilizing Machine Learning in E-Learning Systems,” International Transactions on Artificial Intelligence (ITALIC), vol. 2, no. 1, pp. 76–80, 2023, doi: 10.33050/italic.v2i1.422.

S. Ganesan, U. Subramaniam, A. A. Ghodke, R. M. Elavarasan, K. Raju, and M. S. Bhaskar, “Investigation on Sizing of Voltage Source for a Battery Energy Storage System in Microgrid With Renewable Energy Sources,” IEEE Access, vol. 8, pp. 188861–188874, 2020, doi: 10.1109/ACCESS.2020.3030729.

M. Faisal, S. A. M. Hidayat, A. R. Basrida, and M. T. Fazrin, “Prototype of water level and rainfall detection system as flood warning based on Blynk IOT Application,” International Transactions on Education Technology, vol. 2, no. 1, pp. 1–10, 2023.

M. Adhit Dwi Yuda, “Transformasi Data Solarman Untuk Pengungkapan Informasi dan Pola PLTS dengan Metode Semi-Supervised Learning,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 1, no. 2, pp. 100–110, 2023, doi: 10.34306/mentari.v1i2.145.

A. Oussous, F.-Z. Benjelloun, A. A. Lahcen, and S. Belfkih, “Big Data technologies: A survey,” Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 4, pp. 431–448, 2018.

M. Tomson et al., “Green infrastructure for air quality improvement in street canyons,” Environ Int, vol. 146, p. 106288, 2021.

H. Luo et al., “Air pollution characteristics and human health risks in key cities of northwest China,” J Environ Manage, vol. 269, p. 110791, 2020.

Q. Aini, U. Rahardja, D. Manongga, I. Sembiring, M. Hardini, and H. Agustian, “IoT-Based Indoor Air Quality Using Esp32,” in 2022 IEEE Creative Communication and Innovative Technology (ICCIT), IEEE, 2022, pp. 1–5.

K. Kholil, K. Sulistyadi, and S. Arlan, “Strategies Of Food Safety Program Improvement To Prevent Food Poisioning Outbreak At Oil & Gas,” ADI Journal on Recent Innovation (AJRI) The 1st Edition Vol 1. No 1. September 2019, p. 46, 2020.

D. Y. Paramartha, A. L. Fitriyani, and S. Pramana, “Development of Automated Environmental Data Collection System and Environment Statistics Dashboard,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 2, pp. 314–325, 2021.

P. Friedlingstein et al., “Global carbon budget 2020,” Earth System Science Data Discussions, vol. 2020, pp. 1–3, 2020.

V. Telukunta, J. Pradhan, A. Agrawal, M. Singh, and S. G. Srivani, “Protection challenges under bulk penetration of renewable energy resources in power systems: A review,” CSEE Journal of Power and Energy Systems, vol. 3, no. 4, pp. 365–379, 2017, doi: 10.17775/CSEEJPES.2017.00030.

Y. Gu, Y. Huang, Q. Wu, C. Li, H. Zhao, and Y. Zhan, “Isolation and Protection of the Motor-Generator Pair System for Fault Ride-Through of Renewable Energy Generation Systems,” IEEE Access, vol. 8, pp. 13251–13258, 2020, doi: 10.1109/ACCESS.2020.2965773.

R. M. Elavarasan et al., “A Comprehensive Review on Renewable Energy Development, Challenges, and Policies of Leading Indian States With an International Perspective,” IEEE Access, vol. 8, pp. 74432–74457, 2020, doi: 10.1109/ACCESS.2020.2988011.

L. Meria, “Development of Automatic Industrial Waste Detection System for Leather Products using Artificial Intelligence,” International Transactions on Artificial Intelligence (ITALIC), vol. 1, no. 2, pp. 195–204, 2023, doi: 10.33050/italic.v1i2.296.

Downloads

Published

2024-05-24

How to Cite

Fernandez, M., Faturahman, A., & Santoso, N. A. (2024). Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling. International Transactions on Education Technology (ITEE), 2(2), 173–182. https://doi.org/10.33050/itee.v2i2.595