Multi-step prediction of offshore wind power based on Transformer network and Huber loss

• The integration of the Transformer network, known for understanding extensive sequence correlations, with the Huber loss function optimizes performance in time series data with outliers. This marks the first use of the Huber loss function within the Transformer architecture. • To address offshore wind data volatility, an autoencoder (AE) is employed for data reconstruction and denoising. The slime mould optimization algorithm then enhances hyperparameter and structural optimization, improving estimation accuracy. • This work departs from traditional single-step predictions by introducing a multi-step prediction model, offering richer predictive insights. This paradigm shift augments the reliability of power system operations and adds significant value to offshore wind power management. In the context of the burgeoning expansion of renewable energy sources, the precise prediction of offshore wind power assumes a pivotal role in safeguarding the reliability, economic viability, and sustainable progression of offshore wind farms. The present study introduces a novel methodology for offshore wind power prediction, predicated upon the synergy of the Transformer network and Huber loss function. Empirical validation is conducted utilizing authentic data from a European offshore wind farm. The resulting analyses delineate a discernible superiority of the Transformer network over classical LSTM and GRU models in capturing the intricate long-term dependencies intrinsic to the time series. Furthermore, the inclusion of the Huber loss function effectively mitigates the challenges posed by the high volatility often characteristic of offshore wind power data. The study also demonstrates the beneficial integration of autoencoder reconstruction for denoising and slime mould optimization algorithm to augment prediction performance. Distinctively diverging from traditional single-step prediction paradigms, the multi-step prediction model constructed within this research offers a more comprehensive and precise prediction of wind power. Such an innovative approach represents a valuable contribution to the field, with tangible implications for the dependable operation and future advancement of wind power.