Exploring The Efficiencies Of Bi-LSTM Model and Attention Mechanism In Solar Power Forecasting

Abstract

Solar energy, as a pivotal renewable resource, holds the potential to address global energy demands while mitigating the environmental impact of traditional energy sources. Accurate forecasting of solar energy output is essential for efficient power system management, ensuring grid stability, and facilitating informed decision-making in energy markets. Recent research has highlighted the integration of attention mechanisms into Long Short-Term Memory (LSTM) models, demonstrating their superior predictive capabilities in this domain. This study contributes to the field by exploring a Bidirectional LSTM (BiLSTM) model augmented with an attention mechanism (BiLSTM-Attn), incorporating three time-series features for enhanced solar energy forecasting. The proposed predictive model is empirically compared to multi-variable time series models, revealing the efficiencies of the BiLSTM-Attn. The results are extensively evaluated on real solar power datasets in Vietnam and overseas.