标题： 短期异常负荷的基于规则的三重季节性方法预测 显示英文标题

标题： Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods

摘要： 许多方法已被提出用于预测正常日的负荷。然而，在研究文献中，异常负荷的建模常常被忽略。这种异常负荷条件发生在特殊的日子里，比如公共假期，由于它们的不常见发生和与正常负荷的重大偏差，给建模带来了相当大的挑战。为了克服这些限制，我们采用了一种基于规则的方法，该方法允许将先前的专家负荷模式知识纳入统计模型。我们使用三重季节性霍尔特-温特斯-泰勒（HWT）指数平滑法，三重季节性自回归移动平均（ARMA），人工神经网络（ANNs），以及三重季节性周内奇异值分解（SVD）为基础的指数平滑法。这些方法已经被证明对于建模正常日的负荷具有竞争力。本文的方法论贡献在于展示这些方法如何能够在与基于规则的方法结合使用时，适应于特殊日的负荷建模。所提出的基于规则的方法能够在统一框架下建模正常和异常负荷。使用英国九年的半小时负荷数据，我们评估了从半小时到一天的点预测。两种基于规则的方法的组合产生了最准确的预测。 显示英文摘要

摘要： Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts.