Name
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). |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | source separation algorithm,approximation theory,bayes methods,regression analysis,energy production,microgeneration system,source separation,inla,decentralized production system,statistical algorithms,polynomial regression form,energy consumption,underdetermined source separation problem,power grids,wind power generation signals,bayesian separation,integrated nested laplace approximation,polynomials,latent variable model,wind power plants,electricity grid operators |
Field | DocType | ISSN |
Mathematical optimization,Polynomial,Computer science,Regression analysis,Laplace's method,Latent variable model,Polynomial regression,Approximation theory,Energy consumption,Source separation | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Won Yoon | 1 | 112 | 23.94 |
| Francesco Fusco | 2 | 6 | 2.08 |
| Michael Wurst | 3 | 5 | 1.35 |