The variable nature of wind, including wind speed, direction, barometric pressure, and air temperature, presents significant challenges for accurately predicting wind power output. This paper addresses the issue of contextual prediction accuracy, highlighting limitations of existing methods in analyzing temporal information in depth. It introduces a Transformer-based Dynamic context-aware power forecasting model that combines the Long short-term memory (LSTM) to enhance contextual wind power prediction. The model identifies significant factors influencing wind power generation and integrates various conditions affecting wind power output into a unified embedding. To improve the forecast accuracy, the model adopts a two-layer architecture. The first layer uses LSTM units to extract essential temporal features from the data stream. The subsequent layer utilizes the Dynamic context-aware model's hierarchical multihead self-attention mechanism to discern global information and contextual interrelations. The results reveal that the LSTM-based dynamic context-aware model significantly outperforms other models in forecasting wind power plants output.
Yasir et al. (Sun,) studied this question.