Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function

Abstract

Wind energy technologies have been investigated extensively due to worldwide environmental challenges and rising energy demand. Therefore, accurate and reliable wind speed forecasts are essential for large scale wind power integration. But seasonal and stochastic winds make forecasting difficult. Hence, this study proposes a novel loss function-based hybrid deep learning architecture for the wind speed forecasting (WSF). The proposed hybrid model is developed using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) decomposition method for the denoising wind speed data, and transformer network (TRA) with a novel Kernel MSE loss function (NLF) for the WSF (NLF-ICEEMDAN-TRA). As the MSE loss function is sensitive to outliers and fails to identify the non-linear characteristics in wind speed data, a novel kernel MSE loss function has been used for the training of the transformer network. Wind speed data from two wind farms located in Block Island and the Gulf Coast have been used to validate the effectiveness of the proposed hybrid model. Because current WSF methods performance declines as the time ahead rises, the proposed hybrid model is verified using eight time horizons: 5 min, 10 min, 15 min, 30 min, 1 h, 2 h, 24 h and 48 h ahead WSF. To investigate the performance of proposed hybrid model in WSF, six individual WSF models, six hybrid WSF models, and six NLF based hybrid WSF models are employed for comparative analysis. The experimental results demonstrated that the proposed hybrid model achieved the best results for all eight time horizon WSF for both wind farm sites, with a significant improvement.