Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural...

As the global energy demand continues to produce, photovoltaic (PV) solar energy has emerged as a key Renewable Energy Source (RES) due to its sustainability and potential to reduce dependence on fossil fuels. However, accurate forecasting of Solar Energy (SE) output remains a significant challenge due to the inherent variability and intermittency of solar irradiance (SI), which is affected by factors such as weather conditions, geographic location, and seasonal patterns. Reliable prediction models are crucial for optimizing energy management, ensuring grid stability, and minimizing operational costs. To address these challenges, this research introduces an innovative method that integrates Robust Seasonal-Trend Decomposition (RSTL) with an Adaptive Seagull Optimisation Algorithm (ASOA)-optimized Long Short-Term Memory (LSTM) neural network. Using RSTL to differentiate between time series data into development, seasonal in nature, and residual factors, this methodology addresses SI's unpredictable nature and intermittent operation and provides the basis for accurate predictions. ASOA improves LSTM features by constantly finding and exploiting resources and adopting motivation from seagulls' collecting and migration behaviours. Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. The study of performance measurements (RMSE, MAE, and R²) demonstrates significant improvements in the accuracy of predictions. The research results highlight significant implications regarding subsequent studies and real-world uses in SE prediction, accentuating the positive impacts of incorporating accurate data decomposition and adaptive optimized performance.