Sammenlign metoder

	Prognose af elforbrug	Smart Grid-tilstandsoverslag
Fagområde	Elektroteknik	Elektroteknik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1960s	1970s
Ophavsperson	Electrical utilities	Power systems engineering community
Type	Computational pipeline	Computational pipeline
Oprindelig kilde	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.
Aliasser	demand forecasting, electricity consumption prediction, load demand estimation	state estimation, network state estimation, grid state assessment
Relaterede	4	4
Resumé	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.