Forecasting power system flexibility requirements: A hybrid deep-learning approach

• A hybrid forecasting framework combining CEEMDAN decomposition and ConvLSTM-2D. • Empirical validation on the Occitanie region's power system. • Showcased the model's superiority over traditional forecasting methods. • The methodology is challenged during periods of disruption such as March-April 2022. The rapid growth of Variable and Renewable Energies (VRE) worldwide poses significant challenges to power systems, particularly in managing rapid changes in load and generation. A critical research gap exists in effectively forecasting Residual Load (RL) to enhance power system flexibility, as existing methods often struggle with the volatility and non-linearities introduced by VRE. This paper addresses this gap by proposing a novel hybrid forecasting framework that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Convolutional Long Short-Term Memory (ConvLSTM-2D) Artificial Neural Network (ANN). This framework is designed to more accurately capture complex patterns in RL data, thereby improving forecast accuracy under high volatility conditions. We apply this methodology to forecast half-hourly RL for 2021 in the French region of Occitanie, achieving an average Mean Absolute Percentage Error (MAPE) of 4.29 %. Our results demonstrate that the proposed framework significantly outperforms existing models, offering consistent accuracy over time and proving highly suitable for practical daily use among energy stakeholders.