A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems

A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems

Abstract Solar tracking system efficiency is affected by climate variability, and adaptive mechanisms must be employed to maximize energy output. Conventional fixed-tilt, single-axis, and dual-axis tracking techniques are not real-time adaptive, resulting in energy loss. This paper introduces COMLAT...

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Bibliographic Details
Main Authors: Udit Mamodiya, Indra Kishor, Priyam Ganguly, Isha Mukherjee, Nithesh Naik
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Solar tracking
Deep Q-learning
Climate adaptability
CNN-LSTM
XGBoost
Energy yield optimization
Online Access:https://doi.org/10.1038/s41598-025-10831-3
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https://doi.org/10.1038/s41598-025-10831-3

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