Abstract
A novel off-grid hybrid power generation system for domestic use that can generate electricity using solar photovoltaic (PV) cells and municipal solid waste to power a diesel-alternator power generation system is examined in this paper in relation to Sustainable Development Goal 13. The analysis has been carried out with its diverse year-round climatic conditions. For the solar PV unit, the amount of annual electricity production is 60 kWh, and the annual levelized cost of electricity in ₹/kWh is 4.17. The values of annual average energy efficiency of the hybrid power poly generation system are 18.61%, exergy efficiency 51.94%, and electrical efficiency 13.02% for the 1st energy system as mentioned. In addition, the annual average energy efficiency of the 2nd energy system is 14.17%. Additionally, the energy management system uses a random forest machine learning technique to forecast how best to use resources for maximum productivity and power consistency. For the first energy system, the WPO20BG20 fuel combination represented the best composite score and the ideal weight of fuel types in terms of energy and exergy efficiency. The 2nd energy system model identified the optimal temperature and isolation as 25.86 °C and 4750.30 Wh/m2 for maximum energy efficiency, respectively.
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Authors acknowledge the Government of Odisha for the all data required for the calculations in this work.
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All authors contributed to the study conception and design. Material preparation, and data collection and analysis were performed by Amar Kumar Das, Hitesh Mohapatra, and Soumya Ranjan Mishra. The first draft of the manuscript was written by Amar Kumar Das Overall supervision and project administration were carried out by Sudhansu S. Sahoo. All authors read and approved the final manuscript.
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Das, A.K., Mohapatra, H., Mishra, S.R. et al. Exploring the synergistic potential of a hybrid PV-biogas power generation system for smart city electrification by sustainable thermo-exergetic and environmental analysis using a forest machine learning approach. Environ Sci Pollut Res (2025). https://doi.org/10.1007/s11356-025-37237-y
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DOI: https://doi.org/10.1007/s11356-025-37237-y