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Chapter 16 Energy Decomposition Model Using Takagi-Sugeno Neuro Fuzzy

机译:第16章基于Takagi-Sugeno神经模糊的能量分解模型

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摘要

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:分解分析是确定影响能源消耗的重要因素的有用方法。本文介绍了使用人工智能方法对印尼家庭电力消耗进行分解的因素。在这项研究中使用的人工智能技术是神经模糊高木-Sugeno(NFTS)网络,该网络在多输入多输出条件下工作。通过调整适当的高斯参数(均值和方差)和两个Takagi-Sugeno权重,揭示了电力消耗的变化,这些变化被分解为生产效应,结构效应和效率效应。与常规方法相比,考虑到网络中产生的误差范围在0.003%至2.09%之间,NFTS网络对于恒定价格变量和当前价格变量的性能都非常令人满意,这是非常低的并且可以接受。

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