Compară metode

	Prognoza cererii de energie electrică	Estimarea stării rețelei electrice inteligente
Domeniu	Inginerie electrică	Inginerie electrică
Familie	Process / pipeline	Process / pipeline
Anul apariției	1960s	1970s
Autorul original	Electrical utilities	Power systems engineering community
Tip	Computational pipeline	Computational pipeline
Sursa seminală	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.	Abur, A., & Exposito, A. G. (2004). Power System State Estimation: Theory and Implementation. Marcel Dekker.
Denumiri alternative	demand forecasting, electricity consumption prediction, load demand estimation	state estimation, network state estimation, grid state assessment
Înrudite	4	4
Rezumat	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.	Power system state estimation infers the real-time voltage and phase angle at every bus in a power network from redundant measurements of power flows and voltages. It is the foundation of modern grid operations, enabling real-time monitoring, contingency analysis, and optimal control. Advanced state estimation with synchronized phasor measurements (synchrophasors) enables faster control and detection of instabilities.