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Forecasting power load curves from spatial and temporal mobile data

机译:根据空间和时间移动数据预测电力负荷曲线

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This work aims at applying computational intelligence approaches to telecommunication data, in order to associate mobile data to energy consumption load curves. Clustering methods are applied in order to allow the telecommunication network to infer about its topology and consumption load forecasting. Through an extensive analysis of Telecom Italia dataset and power distribution lines data available for the city of Trento, it was possible to confirm the high correlation between them, mainly when voice data is considered. To a great extent, this correlation can be explained by the fact that cellular communication devices are physically present in the service area of the distribution lines and when people are communicating, they are also consuming energy. Based on the aforementioned dataset, load curves for the city of Trento were constructed having as inputs data from telecommunication transactions. Results show that it is possible to use the telecommunication load as the input to predict the energy load, with the proposed model performing better than the naive predictor in 82% of the tested distribution lines.
机译:这项工作旨在将计算智能方法应用于电信数据,以便将移动数据与能耗负载曲线相关联。应用聚类方法是为了允许电信网络推断其拓扑和消耗负荷预测。通过对特伦托市可用的意大利电信数据集和配电线路数据的广泛分析,可以确认它们之间的高度相关性,主要是在考虑语音数据时。在很大程度上,这种相关性可以通过以下事实来解释:蜂窝通信设备实际上存在于配电线路的服务区域中,并且当人们进行通信时,它们也在消耗能量。基于上述数据集,构建了特伦托市的负荷曲线,并将电信交易的​​数据作为输入。结果表明,可以将电信负载用作预测能量负载的输入,在82%的测试配电线路中,所提出的模型的性能要优于单纯的预测器。

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