FTLNet: federated deep learning model for multi-horizon wind power forecasting

In the competitive market of wind power forecasting, ensuring data security while enhancing forecasting accuracy is crucial. Federated learning emerges as a key solution to these challenges, offering a way to preserve data privacy and security across multiple entities. This paper presents a groundbreaking approach that leverages Federated Transfer Learning (FTL) in conjunction with Additive Attention Temporal Neural Network architectures, focusing on the competitive wind power forecasting. Our method integrates the sophisticated capabilities of Gated Residual Networks with a strategic process of feature selection, tailored for the unique demands of secure and efficient power forecasting. Through experiments involving data from various geographical regions, this research uncovers the specific challenges of applying federated learning in a competitive landscape. The resulting FTL-Net model demonstrates exceptional performance, surpassing existing benchmarks with metrics such as a Mean Absolute Error (MAE) of 6.011, Mean Squared Error (MSE) of 13,855.45, Root Mean Squared Error (RMSE) of 11.7, a coefficient of determination (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}$$\end{document}) of 0.9899, and a correlation coefficient of 0.998. These results not only confirm the FTL-Net model's efficacy but also emphasize the significance of meticulous feature selection in federated learning contexts. Compared to other models, FTL-Net's superior performance, bolstered by feature selection, establishes it as a leading approach in the secure and competitive realm of wind power forecasting. This research underscores the potential of combining advanced neural network architectures with federated learning for future advancements in the field, highlighting the importance of data security and collaborative learning in achieving high accuracy in power forecasting.