手法を比較

	負荷予測	エネルギー貯蔵のディスパッチ最適化
分野	電気工学	電気工学
系統	Process / pipeline	Process / pipeline
提唱年	1960s	2000s
提唱者	Electrical utilities	Utilities and storage technology developers
種類	Computational pipeline	Computational pipeline
原典	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.
別名	demand forecasting, electricity consumption prediction, load demand estimation	battery dispatch, storage scheduling, energy arbitrage optimization
関連	4	4
概要	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.