Neural Network Models for Forecasting Solar Energy Harvesting Efficiency
DOI:
https://doi.org/10.54228/mjaret0624035Keywords:
Solar Energy Forecasting; Neural Networks; Photovoltaic Efficiency; LSTM; Deep Learning; Renewable Energy; Energy Prediction; Smart Grid; Time Series Analysis; Environmental Data Analytics.Abstract
Accurate efficiency prediction of solar power harvesting methods remains crucial to optimize photovoltaic systems. Conventional forecasting methods struggle when weather conditions are unstable because they lack the ability to detect complex nonlinear and time-related patterns. Personnel implementing this study developed a forecasting system through the combination of Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network architecture. The system adopts adaptive learning features alongside multi-feature encoding to deal with different climate patterns. Standard solar dataset evaluation confirmed that the LSTM model achieved best performance by delivering 0.118 RMSE and surpassing 95% accuracy mark. The conducted analysis shows that neural networks particularly LSTM prove to be exceptionally efficient for energy prediction operations. The proposed system provides an efficient scalable approach for managing solar energy while planning smart grids in variable operating conditions.
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