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Fuzzy clustering and prediction of electricity demand based on household characteristics

机译:基于家庭特征的模糊聚类与电力需求预测

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The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into valuable knowledge on the behaviour of consumers. This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data using fuzzy clustering and modelling. The methodology intends to: (1) determine consumer segments based on the metering data using the fuzzy c-means clustering algorithm, and (2) develop Takagi-Sugeno fuzzy models in order to predict the demand profile of the consumers.
机译:过去十年的电力市场在显着变化。智能电表的部署使得能够记录与公用事业公司的运营有关的大量数据,其潜力转化为对消费者行为的有价值的知识。这项工作提出了一种方法,用于使用模糊聚类和建模基于静态数据预测电力使用典型日常负载轮廓。该方法意图:(1)使用模糊C-Means聚类算法基于计量数据确定消费者段,(2)开发Takagi-Sugeno模糊模型,以预测消费者的需求概况。

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