A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition

The ultra-short-term forecasting of wind speed is of great significance to the stable power supply of the power system. Current wind speed forecasting methods aim to improve forecasting precision while disregarding model training speed and model deployment complexity. This research proposes a lightweight hybrid model named SLF-EMD-MGM-NS for wind speed forecasting. EMD-MGM is designed as the network’s fundamental structure for reducing the hybrid model’s training time and ensuring the hybrid model has high forecasting precision. The study presents the switching loss function (SLF) mechanism. When the quantile is 0.5, an MSE-based loss function is employed for training all subsequences. When the quantile is 0.5 and 0.95, first use the wind speed fluctuation threshold to select primary subsequence, and then use the Log-Cosh-based loss function for training primary subsequences. The SLF mechanism can increase point prediction precision and interval prediction boundary stability. Moreover, a novel subsequence superposition (NS) mechanism is proposed for getting high confidence level and narrow-width interval prediction results. The NS mechanism superimposes the interval prediction results of the fluctuation subsequence with the point prediction results of the model to generate the final interval prediction results. According to the experimental results, the SLF-EMD-MGM-NS model has a high confidence level, acceptable prediction results, a narrow-width interval prediction result, and a significantly shorter training time than the other hybrid models.