Bayesian separation of wind power generation signals

Ji Won Yoon, Francesco Fusco, Michael Wurst

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of most challenging and important tasks for electricity grid operators and utility companies is to predict and estimate the precise energy consumption and generation of individual households which have their own decentralized production system. This is a under-determined source separation problem since only the difference between energy production and consumption in the micro-generation system is visible. Therefore, we present a latent variable model with a polynomial regression form for the separation and then the model is used by several statistical algorithms to explore the underlying energy consumption and production from the differenced signals. In order to efficiently find global optima of the hidden variables of the model, we develop a source separation algorithm based on the Integrated Nested Laplace Approximation (INLA).

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages2660-2663
Number of pages4
Publication statusPublished - 2012 Dec 1
Externally publishedYes
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period12/11/1112/11/15

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Yoon, J. W., Fusco, F., & Wurst, M. (2012). Bayesian separation of wind power generation signals. In Proceedings - International Conference on Pattern Recognition (pp. 2660-2663). [6460713]