基于人工神经网络的电力系统负荷预测分解方法

G. Mbamalu, M. El-Hawary
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引用次数: 2

摘要

通过对新斯科舍省电力公司系统负荷进行分解,利用多层神经网络和反向传播算法对商业部门负荷部分进行预测。为了最大限度地减少天气对商业负荷预报的影响,将其进一步分解为四个独立的部分,每个部分持续6小时。训练集的最优输入是根据预测负荷的残差平方和确定的。输入模式由刚刚过去的四或五个小时的负载组成,输出是第五或第六个小时的负载。使用该方法获得的结果证明,在缺乏一些影响变量(如温度)的情况下,仔细选择训练模式将提高人工神经网络在预测电力系统负荷方面的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A decomposition approach to forecasting electric power system commercial load using an artificial neural network
We use a multilayer neural network with a backpropagation algorithm to forecast the commercial sector load portion resulting from decomposing the system load of the Nova Scotia Power Inc. system. To minimize the effect of weather on the forecast of the commercial load, it is further decomposed into four autonomous sections of six hour durations. The optimal input for a training set is determined based on the sum of the squared residuals of the predicted loads. The input patterns are made up of the immediate past four or five hours load and the output is the fifth or the sixth hour load. The results obtained using the proposed approach provide evidence that in the absence of some influential variables such as temperature, a careful selection of training patterns will enhance the performance of the artificial neural network in predicting the power system load.<>
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