צפיות מחירי חשמל: גשר בין דגמי רצפיים, רשתות עצביות ולמידה חיצונית

תקציר מקורי באנגליתarXiv:2601.02856v4 Announce Type: replace Abstract: Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, and enable effective battery optimization. However, developing an accurate prediction model is highly challenging in an uncertain and volatile market environment. For instance, although linear models generally exhibit competitive performance in predicting electricity prices with minimal computational requirements, they fail to capture relevant nonlinear relationships. Nonlinear models, on the other hand, can improve forecasting accuracy with a surge in computational costs. We employ a multivariate hybrid neural architecture that combines linear and nonlinear feed-forward neu