Energy consumption forecasting via order preserving pattern matching

Date
2014-12
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Source Title
IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
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Pages
238 - 242
Language
English
Type
Conference Paper
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Abstract

We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE.

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Keywords
Online learning, Order preserving pattern matching, Sequential prediction, Equivalence classes, Forecasting, Information science, Pattern matching, Exponential numbers, Loss functions, Online learning, Order preserving, Over fitting problem, Prediction tasks, Relative order, Sequential prediction, Energy utilization
Citation
Published Version (Please cite this version)