Comparer des méthodes

	Prévision de la demande	Optimisation de la gestion des systèmes de stockage d'énergie
Domaine	Génie électrique	Génie électrique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s	2000s
Auteur d'origine	Electrical utilities	Utilities and storage technology developers
Type	Computational pipeline	Computational pipeline
Source fondatrice	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.
Alias	demand forecasting, electricity consumption prediction, load demand estimation	battery dispatch, storage scheduling, energy arbitrage optimization
Apparentées	4	4
Résumé	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.