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Use of artificial neural networks as support for energy saving procedures in telecommunications

机译:人工神经网络的使用作为电信节能程序的支持

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In recent time there is a great interest in the field of Energy Saving applied to a wide range of industrial reality such as production plants, services, public transportation company and telecommunication company. The energy consumption in a telecommutation central could raise interesting values, for this reason there is a great interest in applying forecasting techniques at this problem, in fact one of the major issues is related to the power transportation from the electrical production plant to the telecommutation devices, this task require an estimation of the next hours consumption that will be used as a base for the commercial agreement with the production partner, misestimating the future consumption could generate great losses of money. The authors applied successfully Neural Networks using both fully connected feed forward, back propagation (BP) networks and also Boltzmann Machine's(BM) nets in order to obtain more reliable forecast. Particularly the BM offers a range of validity estimation that is very useful for what if analysis and simulation based decision support systems (DSS).
机译:近来,在适用于生产厂房,服务,公共交通公司和电信公司等各种工业现实的节能领域存在巨大兴趣。远程中央的能量消耗可以提高有趣的价值,因为这个问题对应用预测技术有很大兴趣,实际上主要问题与电力生产设备到远程抵消设备的电力运输有关此后,这项任务需要估计下一个小时的消费,将被用作与生产合作伙伴的商业协议的基础,默默化未来的消费可能会产生巨大的金钱损失。作者使用完全连接的馈送前进,后传播(BP)网络以及Boltzmann机器(BM)网的成功应用了神经网络,以获得更可靠的预测。特别是BM提供一系列有效性估计,这对于基于分析和仿真的决策支持系统(DSS)非常有用。

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