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Bayesian Model Averaging of Load Demand Forecasts from Neural Network Models

机译：贝叶斯模型对神经网络模型的负载需求的平均预测

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Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.
机译：与单个神经网络相比，在集合中创建一组内部的神经网络（NN）模型并累积它们可以实现更好的概述能力。神经网络集合旨在为特定问题提供解决方案。许多研究人员和院士都采用了这项NN集合技术，特别是在机器学习中，并已应用于各种工程，医学和信息技术领域。本文提出了一种基于集合中一组神经网络模型的贝叶斯模型平均的负载需求预测的鲁棒聚集方法。本文估算了使用验证数据集的单个NN模型预测系数的矢量。这些又称重量的这些系数等于产生预测的模型的后验概率。然后将这些BMA权重与测试数据集合的NN模型组合使用。通过将贝叶斯效果与简单的平均方法进行比较，观察到通过利用BMA等先进方法来获得益处，以进行预测组合。

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