Σύγκριση μεθόδων

	Πρόβλεψη Φορτίου	Βελτιστοποίηση Απόδοσης Αποθήκευσης Ενέργειας
Πεδίο	Ηλεκτρολογική Μηχανική	Ηλεκτρολογική Μηχανική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1960s	2000s
Δημιουργός	Electrical utilities	Utilities and storage technology developers
Τύπος	Computational pipeline	Computational pipeline
Θεμελιώδης πηγή	Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44-55.	Dunn, B., Kamath, H., & Tarascon, J. M. (2021). Electrical energy storage for the grid: A battery of possibilities. Science, 334(6058), 928-935.
Εναλλακτικές ονομασίες	demand forecasting, electricity consumption prediction, load demand estimation	battery dispatch, storage scheduling, energy arbitrage optimization
Συναφείς	4	4
Σύνοψη	Load forecasting predicts future electrical demand on power systems across various time horizons: minutes to hours (short-term), days to weeks (medium-term), and months to years (long-term). Accurate forecasting is essential for economic dispatch, unit commitment, and system reliability. Methods range from classical statistical regression to modern machine learning approaches.	Energy storage dispatch optimization determines when to charge and discharge battery systems to maximize revenue, minimize grid stress, or support renewable integration. With falling battery costs and increasing variable renewable generation, storage dispatch has become critical for balancing supply and demand in modern power systems.