Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza Distorsiunii Armonice× | Prognoza cererii de energie electrică× | |
|---|---|---|
| Domeniu | Inginerie electrică | Inginerie electrică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1822 | 1960s |
| Autorul original≠ | Jean-Baptiste Joseph Fourier | Electrical utilities |
| Tip | Computational pipeline | Computational pipeline |
| Sursa seminală≠ | IEEE Std 519-1992: IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. 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 ↗ |
| Denumiri alternative | harmonic content analysis, THD analysis, Fourier harmonic decomposition | demand forecasting, electricity consumption prediction, load demand estimation |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. | 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. |
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