This analysis combines decomposition techniques with deep learning to reduce forecasting errors in photovoltaic systems under varying weather conditions.
Key Points
The proposed model reduces root mean squared error by 20% to 7.30 kW, enhancing photovoltaic power forecasting.
Utilizing multi-scale decomposition through noise-adapted methods improves input signal clarity for better accuracy.
A convolutional neural network paired with long short-term memory optimizes hyperparameters via a new optimization algorithm.
The approach highlights robustness and adaptability to variable meteorological conditions, essential for efficient PV scheduling.