Due to the intermittent nature of dynamic line rating (DLR) of overhead lines, DLR forecasting plays an important role in the scheduling of power networks. In the DLR forecasting, the trend and fluctuation of past data are modeled and future DLR values are estimated. Autoregressive model and its variants are expanded to reach accurate forecasting. Such methods apply white noise assumption to account for the DLR fluctuations. Since DLR fluctuations are related to weather condition, the white noise assumption cannot model fluctuations correctly. The Brownian motion has been implemented to meet data fluctuation issues in time series prediction. The Ornstein–Uhlenbeck (OU) process is one of the most widely used group of forecasting methods which consider Brownian motion. However, this approach is able to model a single factor that has never driven over the time. Therefore, implementing this factor is not suitable for forecasting DLR. In this paper, the OU process is extended into an integrated factorized OU to model and predict DLR values by considering hidden factors of DLR such as the weather conditions. The results, which are evaluated by the reference models, illustrate significant improvement in performance of the points and fluctuations of DLR.

中文翻译：

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基于综合分解 Ornstein-Uhlenbeck 过程的动态线路额定值预测

由于架空线路动态线路额定值 (DLR) 的间歇性，DLR 预测在电网调度中起着重要作用。在 DLR 预测中，对过去数据的趋势和波动进行建模，并估计未来的 DLR 值。自回归模型及其变体得到扩展以实现准确预测。此类方法应用白噪声假设来解释 DLR 波动。由于 DLR 波动与天气状况有关，白噪声假设无法正确模拟波动。布朗运动已被实施以解决时间序列预测中的数据波动问题。Ornstein-Uhlenbeck (OU) 过程是最广泛使用的一组考虑布朗运动的预测方法之一。然而，这种方法能够模拟一个从来没有随着时间推移而驱动的因素。因此，实施该因素不适合预测 DLR。在本文中，OU 过程被扩展为一个集成的分解 OU，通过考虑 DLR 的隐藏因素（例如天气条件）来对 DLR 值进行建模和预测。由参考模型评估的结果表明 DLR 的点和波动的性能有显着改善。