Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Slodzes prognozēšana× | Harmoniskā kropļojuma analīze× | |
|---|---|---|
| Nozare | Elektrotehnika | Elektrotehnika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1960s | 1822 |
| Autors≠ | Electrical utilities | Jean-Baptiste Joseph Fourier |
| Tips | Computational pipeline | Computational pipeline |
| Pirmavots≠ | 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 ↗ | IEEE Std 519-1992: IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. link ↗ |
| Citi nosaukumi | demand forecasting, electricity consumption prediction, load demand estimation | harmonic content analysis, THD analysis, Fourier harmonic decomposition |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
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