FDMSNet: A frequency-decomposed multi-scale hybrid framework for renewable energy power forecasting

Accurate renewable energy power forecasting is of great significance for the safe operation, dispatch optimization, and efficient management of modern power systems. However, wind and photovoltaic power generation series usually exhibit significant non-stationarity, volatility, and multi-scale variation characteristics, posing great challenges to medium- and long-term forecasting. To address these issues, this paper proposes FDMSNet, a frequency-decomposed multi-scale hybrid network for renewable energy power forecasting. The model first divides the input sequence into non-stationary and stationary components through a frequency decomposition module. For the non-stationary component, a multilayer perceptron is adopted to extract global nonlinear variation features. For the stationary component, a multi-scale forecasting module is designed based on multi-branch downsampling and cross-scale prediction fusion. Subsequently, the forecasting results of different components are fused to generate the final power prediction. Experiments are conducted on real-world wind farm and photovoltaic power station datasets under multiple prediction horizons. MAE, MSE, and RSE are used as evaluation metrics, and FDMSNet is compared with different types of forecasting models. The experimental results show that FDMSNet achieves strong forecasting accuracy and stability in both wind and photovoltaic power forecasting tasks. Further average ranking analysis, significance testing, ablation experiments, and parameter sensitivity analysis verify the effectiveness and robustness of the frequency decomposition module, the multi-scale modeling structure, and the key parameter settings.