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.