Modelling the load curve of aggregate electricity consumption using principal components
Modelling the load curve of aggregate electricity consumption using principal components
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DOI：
10.1016/j.envsoft.2004.09.019
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发表时间：
2005-11-01
影响因子：
4.9
通讯作者：
Marzullo, A
中科院分区：
文献类型：
--
作者：
Manera, M;Marzullo, A
由于石油是一种对环境影响很大的不可再生资源，而且其最常见的用途是生产电力用可燃物，因此可靠的电力消耗建模方法有助于更合理地利用这种碳氢化合物燃料。在本文中，我们应用主成分（PC）方法根据每小时总用电量数据对意大利、法国和希腊的负荷曲线进行建模。将使用 PC 方法获得的经验结果与傅立叶和约束平滑样条估计器产生的结果进行比较。 PC 方法是电力消耗建模的一种更简单且更具吸引力的替代方法，因为它非常容易计算，显着减少了需要考虑的变量数量，并且通常提高了电力消耗预测的准确性。作为另一个优势，PC 方法能够适应相关的外生变量，例如每日温度和环境因素，并且在计算样本外预测方面用途极其广泛。 (c) 2004 Elsevier Ltd. 保留所有权利。
Since oil is a non-renewable resource with a high environmental impact, and its most common use is to produce combustibles for electricity, reliable methods for modelling electricity consumption can contribute to a more rational employment of this hydrocarbon fuel. In this paper we apply the Principal Components (PC) method to modelling the load curves of Italy, France and Greece on hourly data of aggregate electricity consumption. The empirical results obtained with the PC approach are compared with those produced by the Fourier and Constrained Smoothing Spline estimators. The PC method represents a much simpler and attractive alternative to modelling electricity consumption since it is extremely easy to compute, significantly reduces the number of variables to be considered, and generally increases the accuracy of electricity consumption forecasts. As an additional advantage, the PC method is able to accommodate relevant exogenous variables such as daily temperature and environmental factors, and is extremely versatile in computing out-of-sample forecasts. (c) 2004 Elsevier Ltd. All rights reserved.