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Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface

机译：使用具有专家系统接口的人工神经网络对埃及统一网格每小时负荷进行预测的结果

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This paper presents the hourly load forecasting results of the Egyptian unified grid (EUG). The technique is based on a generalized model combining the features of ANN and an expert system. The above methodology makes the technique robust, updatable and provides for operator intervention when necessary. This property makes it especially suitable for the EUG where the load patterns are influenced mostly because of social activities, and weather contributes very little to load forecast. For example, many social occasions depend on religious preferences which cannot be decided well in advance. This technique has been tested with one year data of EUG during 1993. The results clearly demonstrate the advantage of the above methodology over statistical based techniques. The average absolute forecast errors for the proposed methodology is 2.63% with a standard deviation of 2.62% whereas, the conventional multiple regression method scores an average absolute error of 4.69% with a standard deviation of 4.03%.
机译：本文介绍了埃及统一电网（EUG）的每小时负荷预测结果。该技术基于结合了人工神经网络和专家系统特征的通用模型。上述方法使该技术健壮，可更新，并在必要时提供操作员干预。此属性使其特别适合EUG，EUG的负荷模式主要是由于社交活动而受到影响，而天气对负荷预测的贡献很小。例如，许多社交场合取决于宗教偏好，而宗教偏好无法事先确定。该技术已经在1993年用EUG的一年数据进行了测试。结果清楚地证明了上述方法相对于基于统计的技术的优势。所提出的方法的平均绝对预测误差为2.63％，标准偏差为2.62％，而常规多元回归方法的平均绝对误差为4.69％，标准偏差为4.03％。

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