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Chapter 16 Energy Decomposition Model Using Takagi-Sugeno Neuro Fuzzy

机译:第16章使用Takagi-sugeno neuro模糊的能量分解模型

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Decomposition analysis is useful method to determine significant factors contribute towards the development of energy consumption. This paper presents factors decomposition of electricity consumption in Indonesia's household sector using artificial intelligent method. The proposed artificial intelligent technique used in this study is the Neuro Fuzzy Takagi-Sugeno (NFTS) network, which is worked under multiple input multiple output condition. By tuning the appropriate Gaussian parameters, which are mean and variance, and two Takagi-Sugeno weight, the changes in electricity consumption that is decomposed into production effect, structural effect, and efficiency effect, has revealed. Compared to the common method, the performance of NFTS network for both constant and current price variables is quite satisfied, given the error generated in the network ranges between 0.003 and 2.09 %, which is quite low and acceptable.
机译:分解分析是确定重要因素有助于发展能耗的有用方法。本文采用人工智能方法提出了印度尼西亚家庭部门电力消耗的因素。本研究中使用的拟议的人工智能技术是神经模糊Takagi-Sugeno(NFTS)网络,其在多输入多输出条件下工作。通过调整适当的高斯参数,这是均值和方差,以及两个Takagi-sugeno重量,揭示了分解为生产效果,结构效果和效率效应的电力消耗的变化。与常见方法相比,对于常量和当前价格变量的NFTS网络的性能非常满意,鉴于网络中生成的错误0.003和2.09%,这非常低,可接受。

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