A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System
K Bala Prasad,J. Samson Isaac,P. Ponsudha,N. Nithya,Santaji Shinde,S Raja Gopal,Atul Sarojwal,K Karthikumar,Kibrom Menasbo Hadish +8 more
TL;DR: In this paper , a machine learning-based LFR technique has been proposed for flat-plate photovoltaic (PV) systems, where the LFR is less than 30 mm wide to maximize thermal efficiency and densely packed cell array has been used to maximize electrical output.
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Abstract: Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.
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Citations
Machine Learning-Based Optimization of Wind-PV Solution for Grid Demand
Malathi Govindasamy,A.M.J. Zubair Rahman,S.D Vijayakumar,Karthikeyan Jayabalan +3 more
TL;DR: This study proposes a machine learning-based optimization method for Wind-PV systems, leveraging CNN and genetic algorithms to enhance capacity utilization and accuracy, reducing mean absolute error from 8% to 4.5% and increasing capacity utilization from 73% to 92%.
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