This paper introduces a novel hybrid recurrent neural network architecture that integrates linear structures like expert models and Kalman filters for day-ahead electricity price forecasting. The model achieves approximately 12% higher accuracy than leading benchmarks by effectively capturing both linear and nonlinear price characteristics in power markets.

This paper introduces the Spiking-Quantum Data Re-upload Convolutional Neural Network (SQDR-CNN), a novel architecture that enables joint training of spiking neural networks and quantum circuits. The model achieves competitive accuracy with significantly fewer parameters than state-of-the-art SNN baselines, showcasing the potential of hybrid neuromorphic and quantum systems for efficient machine learning.