Comparer des méthodes

	Prévision de la demande	Analyse de la distorsion harmonique
Domaine	Génie électrique	Génie électrique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s	1822
Auteur d'origine	Electrical utilities	Jean-Baptiste Joseph Fourier
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.	IEEE Std 519-1992: IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems.
Alias	demand forecasting, electricity consumption prediction, load demand estimation	harmonic content analysis, THD analysis, Fourier harmonic decomposition
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.	Harmonic distortion analysis quantifies the deviation of voltage or current waveforms from sinusoidal shape due to nonlinear loads. Using Fourier decomposition, engineers separate the waveform into its fundamental frequency and harmonic components (integer multiples of 50 or 60 Hz). Harmonic analysis is critical for assessing power quality and designing filters in modern power systems with high penetration of nonlinear devices.