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引用次数: 1

摘要

在荷兰目前的电力部门设计中,网络运营商每天都会从测量公司收到有关其客户每一刻钟的用电量的数据。网络运营商根据项目责任方(PRP)和网络区域的组合对数据进行分类,并将分配的数据发送给PRP。测量系列有时似乎包含误差。这些错误需要在短时间内纠正。为了避免将错误的数据转发给prp,网络运营商希望在发送错误数据之前检查错误数据。本文表明,由于数据的一阶自相关非常高,可以很容易地预测用电量。一种简单的预测方法与使用指数平滑模型进行了比较,指数平滑模型最多只能给出稍好的结果,但使用起来要复杂得多。
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Supporting the electricity network operator in the allocation process
In the current design of the electricity sector in the Netherlands, network operators daily receive data from measurement companies about the electricity consumption of their customers for each quarter of an hour. The network operator sorts the data per combination of Program Responsible Party (PRP) and network district region and sends the allocated data to the PRPs. The measurement series sometimes appear to contain errors. These errors need to be corrected in a short time. To avoid forwarding wrong data to the PRPs, the network operator would like to check the data on errors before they are sent. This paper shows that the electricity consumption can easily be predicted because the first-order autocorrelation of the data is very high. A simple prediction method is compared with the use of exponential smoothing models which give only slightly better results at best, but are much more complex to use.
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