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Improvement of MLP models for forecasting electrical energy consumption using OBD and OBS methods

机译：使用OBD和OBS方法改进MLP模型以预测电能消耗

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In this paper we apply two reduction algorithms of a neural network architecture in order to improve the prediction quality of a multilayer perceptron network (MLP). The first algorithm is Optimal Brain Damage (OBD), whereas the second is Optimal Brain Surgeon (OBS). Our assumptions have been verified experimentally on the models for electricity consumption prediction using real data from the Polish electroenergetic system. Two series of tests have been carried out: the first is hourly forecast of electricity consumption for twenty four hours ahead, and the second is daily forecast of electricity consumption for one day ahead. Taking into account results of performed computations, we have found out that both algorithms OBD and OBS improve the prediction quality of an MLP network. Moreover, simplification of the network speeds up the training process. Presumably, we can assume that these conclusions can be expanded to other time series prediction tasks.
机译：在本文中，我们应用了神经网络架构的两种归约算法，以提高多层感知器网络（MLP）的预测质量。第一种算法是最佳脑损伤（OBD），而第二种算法是最佳脑外科医生（OBS）。我们的假设已通过波兰电力系统的真实数据在用电量预测模型上进行了实验验证。已经进行了两个系列的测试：第一个是提前二十四小时的每小时用电量预测，第二个是提前一天的每日用电量预测。考虑到执行计算的结果，我们发现算法OBD和OBS均可提高MLP网络的预测质量。此外，简化网络可以加快培训过程。据推测，我们可以假定这些结论可以扩展到其他时间序列预测任务。

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