Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Jari Miettinen"'
Publikováno v:
Journal of Statistical Software, Vol 98, Iss 1 (2021)
Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing t
Externí odkaz:
https://doaj.org/article/3fae56349f204d4dae523d2280318db0
Publikováno v:
Journal of Statistical Software, Vol 76, Iss 1, Pp 1-31 (2017)
Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The m
Externí odkaz:
https://doaj.org/article/62226f3c14c74ee29a20a558872d432a
Publikováno v:
Austrian Journal of Statistics, Vol 46, Iss 3-4 (2017)
Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract
Externí odkaz:
https://doaj.org/article/c8421e6435fa41cda54f8df0464ab4fa
Publisher Copyright: © 2021 The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e86a6e93d48972e42e52c02f3b80d7a
https://aaltodoc.aalto.fi/handle/123456789/109063
https://aaltodoc.aalto.fi/handle/123456789/109063
Publikováno v:
Miettinen, J, Holttinen, H & Hodge, B M 2020, ' Simulating wind power forecast error distributions for spatially aggregated wind power plants ', Wind Energy, vol. 23, no. 1, pp. 45-62 . https://doi.org/10.1002/we.2410
Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power prod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fcb5460cba61adae4a4364b25eb078e
https://doi.org/10.1002/we.2410
https://doi.org/10.1002/we.2410
Autor:
Jari Miettinen, Hannele Holttinen
Publikováno v:
Miettinen, J & Holttinen, H 2019, ' Impacts of wind power forecast errors on the real-time balancing need : A Nordic case study ', IET Renewable Power Generation, vol. 13, no. 2, pp. 227-233 . https://doi.org/10.1049/iet-rpg.2018.5234
As the share of wind power increases in the power system, also the share of uncertain generation increases. This, in turn, increases the total balancing energy, real-time balancing power requirements and ramping of the balancing generation. In this s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b184ed7af94a5ba1d5adb420bddcef1
https://doi.org/10.1049/iet-rpg.2018.5234
https://doi.org/10.1049/iet-rpg.2018.5234
Publikováno v:
ICASSP
With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior infor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f787e53e764663b2c275bb6a5bdebe57
https://doi.org/10.1109/icassp40776.2020.9054575
https://doi.org/10.1109/icassp40776.2020.9054575
Publikováno v:
Journal of Statistical Software, Vol 76, Iss 1, Pp 1-31 (2017)
Journal of Statistical Software; Vol 76 (2017); 1-31
Journal of Statistical Software; Vol 76 (2017); 1-31
Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a52689e779821ac6ba43c847402bf161
https://www.jstatsoft.org/index.php/jss/article/view/2993
https://www.jstatsoft.org/index.php/jss/article/view/2993
Publikováno v:
ICASSP
Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dc8b322183c14bbdd23bc0f62981b74
https://doi.org/10.1109/icassp.2019.8683193
https://doi.org/10.1109/icassp.2019.8683193
Autor:
Hannele Holttinen, Jari Miettinen
Publikováno v:
Wind Energy. 20:959-972
The growing proportion of wind power in the Nordic power system increases day-ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, becaus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4db46fabb85d6d16ce058dce80f07cb2
https://doi.org/10.1002/we.2073
https://doi.org/10.1002/we.2073