A Hybrid EMD–LASSO–MCQRNN–KDE Framework for Probabilistic Electric Load Forecasting Under Renewable Integration

Abstract

Accurate probabilistic load forecasting is essential for secure power system operation and efficient energy management, particularly under increasing renewable integration and demand-side complexity. However, traditional forecasting methods often struggle with issues such as non-linearity, non-stationarity, feature redundancy, and quantile crossing, which hinder reliable uncertainty quantification. To overcome these challenges, this study proposes a hybrid probabilistic load forecasting framework that integrates empirical mode decomposition (EMD), LASSO-based feature selection, and a monotone composite quantile regression neural network (MCQRNN) enhanced with kernel density estimation (KDE). First, EMD decomposes the raw load series into intrinsic mode functions and a trend component to mitigate non-stationarity. Then, LASSO selects the most informative features from both the decomposed components and the original time series, effectively reducing dimensionality and multicollinearity. Subsequently, the proposed MCQRNN model generates multiple quantiles under monotonicity constraints, eliminating quantile crossing and improving multi-quantile coherence through a composite loss function. Finally, Gaussian kernel density estimation reconstructs a continuous probability density function from the predicted quantiles, enabling full distributional forecasting. The framework is evaluated on two public datasets—GEFCom2014 and ISO New England—using point, interval, and density evaluation metrics. Experimental results demonstrate that the proposed EMD–LASSO–MCQRNN–KDE model outperforms benchmark approaches in both point and probabilistic forecasting, providing a robust and interpretable solution for uncertainty-aware grid operation and energy planning.