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| 智能电网状态估计× | 负荷预测× | |
|---|---|---|
| 领域 | 电气工程 | 电气工程 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1970s | 1960s |
| 提出者≠ | Power systems engineering community | Electrical utilities |
| 类型 | Computational pipeline | Computational pipeline |
| 开创性文献≠ | Abur, A., & Exposito, A. G. (2004). Power System State Estimation: Theory and Implementation. Marcel Dekker. DOI ↗ | 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 ↗ |
| 别名 | state estimation, network state estimation, grid state assessment | demand forecasting, electricity consumption prediction, load demand estimation |
| 相关 | 4 | 4 |
| 摘要≠ | Power system state estimation infers the real-time voltage and phase angle at every bus in a power network from redundant measurements of power flows and voltages. It is the foundation of modern grid operations, enabling real-time monitoring, contingency analysis, and optimal control. Advanced state estimation with synchronized phasor measurements (synchrophasors) enables faster control and detection of instabilities. | 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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