Comparison of the forecasting accuracy of neural networks with other established techniques

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213493

M. Casey Brace, J. Schmidt, M. Hadlin

引用次数: 55

Abstract

A comparison of the forecast accuracy of artificial neural networks is made to other more established forecasting methodologies. Eight different types of forecasts were developed on a daily basis for five months and results analyzed. The MAPE (mean absolute percent error) was computed for each model. The series being forecast was the total system load for the Puget Sound Power and Light Company. The performance of the neural nets was disappointing with all but one of the other techniques outperforming them. Although the neural nets did not do well in this competition, this may be caused by a lack of forecasting experience by the neural net developers rather than limitations in the abilities of nets themselves. Forecasts made with neural nets using the same inputs showed dramatic improvements but the performance was still not as good as the best regression forecast.<>

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神经网络与其他已有技术预测精度的比较

将人工神经网络的预测精度与其他较成熟的预测方法进行了比较。在五个月的时间里，每天都有八种不同类型的预测，并对结果进行了分析。计算每个模型的平均绝对误差百分比(MAPE)。预测的系列是普吉特声音电力和照明公司的总系统负荷。神经网络的表现令人失望，除了一种技术之外，其他技术的表现都优于它们。虽然神经网络在这次比赛中表现不佳，但这可能是由于神经网络开发人员缺乏预测经验，而不是神经网络本身能力的限制。使用相同输入的神经网络进行的预测显示出显著的改进，但性能仍然不如最佳回归预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems

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