Scalable Energy Disaggregation Via Successive Submodular Approximation
Scalable Energy Disaggregation Via Successive Submodular Approximation
复制标题
通过连续子模近似进行可扩展的能量分解
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Sidiropoulos
中科院分区：
文献类型：
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作者：
Faisal M. Almutairi;Aritra Konar;N. Sidiropoulos
能源分解是将家庭的综合读数分解为其组成部分的任务。在本文中，我们提出了一个有监督的非参数框架，以进行分解。我们证明，问题等同于最大化设置功能对组合约束，这是其一般形式的NP-束缚。提出了一种简单的多项式连续近似算法，该算法将每组块的下二次块利用迭代性最大化目标函数的全局下限序列，以获得近似溶液。实际数据的实验表明，我们的方法的优越分类性能和可伸缩性超过了基于凸松弛的最先进的参数阶乘隐藏的基于马尔可夫模型的框架。
Energy disaggregation is the task of decomposing the aggregated power consumption readings of a household into its constituent parts. In this paper, we propose a supervised, non-parametric framework for energy disaggregation. We demonstrate that the problem is equivalent to maximizing a set-function subject to combinatorial constraints, which is NP-hard in its general form. A simple polynomial-time successive approximation algorithm which exploits submodularity per set-block to iteratively maximize a sequence of global lower bounds of the objective function is proposed for obtaining approximate solutions. Experiments on real data indicate the superior disaggregation performance and scalability of our approach over a state-of-the-art parametric Factorial Hidden Markov Model based framework employing convex relaxation.
影响因子：
2.7
作者：
F. Facchinei;Lorenzo Lampariello;G. Scutari
通讯作者：
F. Facchinei;Lorenzo Lampariello;G. Scutari
DOI：
10.1609/aaai.v31i1.11179
发表时间：
2017-02
期刊：
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影响因子：
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作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
通讯作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse