Active Collaborative Sensing for Energy Breakdown
Active Collaborative Sensing for Energy Breakdown
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DOI：
10.1145/3357384.3357929
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Yiling Jia;Nipun Batra;Hongning Wang;K. Whitehouse
中科院分区：
文献类型：
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作者：
Yiling Jia;Nipun Batra;Hongning Wang;K. Whitehouse
住宅约占全球总能源使用量的四分之一。提供电器级的能源细分已被证明可以引起积极的行为变化，可以减少15%的能源消耗。现有的能量分解方法要么需要在每个目标家庭中安装硬件，要么需要大量可用于模型训练的能量传感器数据。然而，世界上很少有家庭安装了分表（测量单个电器能量的传感器）;并且用广泛的分表改造家庭的成本会消耗可用于节能改造的资金。因此，战略部署传感硬件，以最大限度地提高非仪表化家庭中的亚计量读数的重建精度，同时最大限度地降低部署成本变得必要和有前途。在这项工作中，我们开发了一个主动学习解决方案的基础上，低秩张量完成能量分解。我们建议积极部署能源传感器，从选定的家庭电器，目标是提高预测精度的完成张量与最小的传感器部署成本。我们对2013年至2017年在美国德克萨斯州奥斯汀收集的最大公共能源数据集进行了实证评估。结果表明，我们的方法提供了更好的性能与固定数量的传感器安装，相比，国家的最先进的，这也证明了我们的理论分析。
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.