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Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

机译：使用智能电表数据的深度学习辅助建筑能耗剖析

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摘要

The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.
机译：人口的指数增长及其对电气和电子设备使用的整体依赖增加了对能源生产的需求。它需要精确的能源管理系统，可以预测消费者的使用情况，以便将来制定政策。附在电表和家用电器上的嵌入式智能传感器使电力供应商可以有效地分析能源使用情况，以根据他们的能源消耗水平来向居民区发电和分配电力。因此，本文提出了一种基于聚类的能耗分析，将消费者的用电量分为不同级别。首先，训练了一种将低维能耗数据转换为高级表示的深度自动编码器。其次，将高级表示形式输入到自适应自组织图（SOM）聚类算法中。之后，通过对获得的聚类数据进行统计分析，确定电力消耗水平。最后，将结果显示在图表和日历视图中，并将预计的能耗水平绘制在城市地图上，从而为提供商进行能源利用分析提供了紧凑的概览。

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