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Temperature anomaly detection for electric load forecasting

机译:温度异常检测,用于电力负荷预测

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摘要

Since temperature variables are used in many load forecasting models, the quality of historical temperature data is crucial to the forecast accuracy. The raw data collected by local weather stations and archived by government agencies often include many missing values and incorrect readings, and thus cannot be used directly by load forecasters. As a result, many power companies today purchase data from commercial weather service vendors. Such quality-controlled data may still have many defects, but many load forecasters have been using them in full faith. This paper proposes a novel temperature anomaly detection methodology that makes use of the local load information collected by power companies. The effectiveness of the proposed method is demonstrated through two public datasets: one from the Global Energy Forecasting Competition 2014 and the other from ISO New England. The results show that the accuracy of the final load forecasts can be enhanced by removing the detected observations from the original input data. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译:由于许多负载预测模型中都使用了温度变量,因此历史温度数据的质量对于预测准确性至关重要。由当地气象站收集并由政府机构存档的原始数据通常包含许多缺失值和不正确的读数,因此,负荷预报员无法直接使用。结果,今天许多电力公司从商业气象服务供应商那里购买数据。这样的质量控制数据可能仍然存在许多缺陷,但是许多负载预测人员一直在充分使用它们。本文提出了一种新颖的温度异常检测方法,该方法利用了电力公司收集的本地负荷信息。通过两个公共数据集证明了该方法的有效性:一个来自2014年全球能源预测大赛,另一个来自ISO新英格兰。结果表明,通过从原始输入数据中删除检测到的观测值,可以提高最终负荷预测的准确性。 (C)2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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  • 来源
    《International journal of forecasting》 |2020年第2期|324-333|共10页
  • 作者

  • 作者单位

    Univ North Carolina Charlotte Syst Engn Management Charlotte NC 28223 USA;

    North Carolina Assoc Elect Cooperatives Raleigh NC USA;

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  • 正文语种 eng
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