Decomposition-Based Stacked Bagging Boosting Ensemble for Dynamic Line Rating Forecasting

Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing stability, accommodating high renewable energy penetration. Due to the non-stationarity stochasticity meteorological variables, a single model often not sufficient accurately predict rating. Herein, new stacked bagging boosting ensemble developed based on multivariate empirical mode decomposition overcome models' restrictions increase forecasting performance. The utilized data gathered from 400 kV aluminum conductor steel-reinforced power length 32.85 Km between Ghadamgah Binalood wind farms, located northeast Iran. simulation results substantiate that proposed can capture variables' non-linear characteristics, yielding more accurate yet noisy forecasts than models.