Literature Review on Solar Power Prediction Using Bi-LSTM Classifier Considering Different Uncertainty Models

Author(s)	Mr. TATOBA NAMDEO KHANDEKAR, Dr. PRASAD D KULKARNI, Prof. Dr. SHRINIVASA MAYYA D
Country	India
Abstract	Accurate solar power prediction is critical for the efficient operation and integration of photovoltaic (PV) systems into modern power grids. The inherent uncertainty in weather conditions and sensor data presents major challenges for forecasting accuracy. While recent advances in deep learning, particularly using LSTM and its variants, have significantly improved performance, these models often ignore uncertainty in input data. This literature review critically analyzes state-of-the-art techniques for solar power forecasting, highlighting the role of metaheuristic optimization, hybrid neural models, and deep learning algorithms. Special attention is given to recent studies utilizing Bi-LSTM networks and the incorporation (or lack thereof) of uncertainty models. The review identifies key limitations in current approaches and underscores the need for a framework that explicitly integrates uncertainty distributions with Bi-LSTM for enhanced prediction accuracy.
Keywords	Solar power,Bi-LSTM networks,Uncertainy models
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-17
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7048
Short DOI	https://doi.org/g9t2w9

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International Journal on Science and Technology