发表机构
TUD Dresden University of Technology; TransnetBW GmbH（德累斯顿工业大学; TransnetBW有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电能质量参数中期预测，本文基于德国输电网2,807个时间序列，通过基准测试和模型无关特征（SVD熵、波峰因数）评估可预测性，以区分可自动预测与需人工审查的时间序列。
AI 中文摘要
电能质量（PQ）参数的中期预测，时间跨度从数周到约一年，支持输电网监测中的主动维护和限值超标的早期检测。然而，其实际价值取决于事先了解哪些时间序列能够被可靠预测。本文基于德国输电网66个站点长期测量活动中获取的2,807个电能质量参数周时间序列（覆盖110 kV、220 kV和380 kV电压等级），分两个阶段解决该问题。首先，对八种预测模型进行基准测试。STL-ARIMA混合模型取得了最高精度，平均sMAPE为17.63%，并且对72%的时间序列，其误差低于季节性朴素基准，而精度在不同电能质量参数和测量站点间差异显著。其次，仅从训练集计算的模型无关特征被评估为内在可预测性的度量。SVD熵和波峰因数与所有八个模型的实际预测精度强相关，并被用于逻辑回归，以在任何模型应用前估计预测效果不佳的概率。由此产生的度量使运维人员能够将适合自动预测的时间序列与需要人工审查的时间序列区分开来。
英文摘要
Medium-term forecasting of Power Quality (PQ) parameters, on horizons of weeks to about one year, supports proactive maintenance and the early detection of limit exceedances in transmission network monitoring. Its practical value, however, depends on knowing in advance which time series can be forecast reliably at all. This article addresses that question in two stages, based on 2,807 weekly time series of PQ parameters from a long-term measurement campaign at 66 sites in the German transmission system, covering the 110 kV, 220 kV, and 380 kV levels. First, eight forecasting models are benchmarked. The STL-ARIMA hybrid achieves the highest accuracy, with an average sMAPE of 17.63% and a lower error than the seasonal naive benchmark for 72% of the time series, while accuracy varies substantially across PQ parameters and measurement sites. Second, model-free features computed from the training set alone are evaluated as measures of intrinsic predictability. SVD entropy and the Crest Factor correlate strongly with the realized forecast accuracy of all eight models and are used in a logistic regression to estimate the probability of a poor forecast before any model is applied. The resulting measures allow operators to separate time series suitable for automated forecasting from those requiring manual review.
Comments10 pages, 11 figures, 2 tables. Preprint, not peer reviewed
