Comparer des méthodes

	Évaluation de la qualité de l'énergie	Prévision de la demande
Domaine	Génie électrique	Génie électrique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995	1960s
Auteur d'origine	IEEE Standards committee	Electrical utilities
Type	Computational pipeline	Computational pipeline
Source fondatrice	IEEE Std 1159-2019: IEEE Recommended Practice for Monitoring Electric Power Quality.	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.
Alias	PQ assessment, power quality survey, voltage quality analysis	demand forecasting, electricity consumption prediction, load demand estimation
Apparentées	4	4
Résumé	Power quality assessment evaluates the suitability of electrical voltage and current waveforms for reliable equipment operation. It measures deviations from ideal sinusoidal waveforms, including voltage sags, swells, harmonics, transients, and imbalance. Comprehensive assessment is critical for ensuring equipment protection, identifying root causes of malfunctions, and optimizing mitigation strategies.	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.