Extracting underlying trend and predicting power usage via joint SSA and sparse binary programming

Yang, Zhijing and Ling, Wing-Kuen and Bingham, Chris (2013) Extracting underlying trend and predicting power usage via joint SSA and sparse binary programming. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), 19-23 May 2013, Beijing, China.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

This paper proposes a novel methodology for extracting the underlying trend and predicting the power usage through a joint singular spectrum analysis (SSA) and sparse binary programming approach. The underlying trend is approximated by the sum of a part of SSA components, in which the total number of the SSA components in the sum is minimized subject to a specification on the maximum absolute difference between the original signal and the approximated underlying trend. As the selection of the SSA components is binary, this selection problem is to minimize the L0 norm of the selection vector subject to the L∞ norm constraint on the difference between the original signal and the approximated underlying trend as well as the binary valued constraint on the elements of the selection vector. This problem is actually a sparse binary programming problem. To solve this problem, first the corresponding continuous valued sparse optimization problem is solved. That is, to solve the same problem without the consideration of the binary valued constraint. This problem can be approximated by a linear programming problem when the isometry condition is satisfied, and the solution of the linear programming problem can be obtained via existing simplex methods or interior point methods. By applying the binary quantization to the obtained solution of the linear programming problem, the approximated solution of the original sparse binary programming problem is obtained. Unlike previously reported techniques that require a pre-cursor model or parameter specifications, the proposed method is completely adaptive. Experiment results show that our proposed method is very effective and efficient for extracting the underlying trend and predicting the power usage. © 2013 IEEE

Keywords:Approximated solutions, Binary quantization, Interior point methods, Linear programming, Maximum absolute differences, Parameter specification, Singular spectrum analysis, Sparse optimizations
Subjects:H Engineering > H600 Electronic and Electrical Engineering
Divisions:College of Science > School of Engineering
ID Code:11974
Deposited On:13 Dec 2013 09:46

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