Matrix Factorisation for Scalable Energy Breakdown
Matrix Factorisation for Scalable Energy Breakdown
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DOI：
10.1609/aaai.v31i1.11179
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
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文献类型：
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作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
房屋占总能源消耗的三分之一以上。已经证明，为房屋产生能源破裂可将家庭能源消耗降低多达15％，以及其他收益。但是，现有的产生能量故障的方法需要在每个房屋中安装硬件，因此非常昂贵。在本文中，我们提出了一种基于特征矩阵分解的新颖应用，该应用不需要任何其他硬件安装。我们方法的基本前提是，房屋的常见设计和构造模式在其能量数据中创造了重复结构。因此，稀疏的基础可用于表示来自广泛房屋的能量数据。我们从公开可公开的数据集中评估了516套房屋的方法，发现它比需要在每个房屋中进行感测的五种基线方法，或对大量房屋以及复杂建模的大量房屋进行非常严格的调查。我们还将系统部署为实时Web应用程序，可能会为数百万户主提供能源故障。
Homes constitute more than one-thirds of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes.