Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Evaluación de la Calidad de la Potencia× | Predicción de Carga× | |
|---|---|---|
| Campo | Ingeniería eléctrica | Ingeniería eléctrica |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1995 | 1960s |
| Autor original≠ | IEEE Standards committee | Electrical utilities |
| Tipo | Computational pipeline | Computational pipeline |
| Fuente seminal≠ | IEEE Std 1159-2019: IEEE Recommended Practice for Monitoring Electric Power Quality. link ↗ | 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. DOI ↗ |
| Alias | PQ assessment, power quality survey, voltage quality analysis | demand forecasting, electricity consumption prediction, load demand estimation |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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