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pro vyhledávání: '"Peter Laurinec"'
Publikováno v:
Journal of Intelligent Information Systems. 53:219-239
This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster an
Autor:
Mária Lucká, Peter Laurinec
Publikováno v:
Data Mining and Knowledge Discovery. 33:413-445
This paper presents a new interpretable approach for multiple data streams clustering in a smart grid used for the improvement of forecasting accuracy of aggregated electricity consumption and grid analysis named ClipStream. Consumers time series str
Autor:
Peter Laurinec, Mária Lucká
Publikováno v:
Open Computer Science, Vol 8, Iss 1, Pp 38-50 (2018)
This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030166809
Proposed paper presents a new model-based Gaussian clustering method and defines new optimization criteria for model-based clustering, which are used as fitness functions in genetic algorithm. These optimization criteria are based on different proper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b81134963149c3dd9681b130f6d7882
https://doi.org/10.1007/978-3-030-16681-6_14
https://doi.org/10.1007/978-3-030-16681-6_14
Autor:
Peter Laurinec, Gabriela Grmanová, Mária Lucká, Marek Loderer, Petra Vrablecová, Viera Rozinajová, Anna Bou Ezzeddine, Peter Lacko
Publikováno v:
International Journal of Hybrid Intelligent Systems. 13:99-112
The complexity of certain problems causes that classical methods for finding exact solutions have some limitations. In this paper we propose an incremental heterogeneous ensemble model for time series prediction where biologically inspired algorithms
Autor:
Peter Laurinec, Mária Lucká
Publikováno v:
New Frontiers in Mining Complex Patterns ISBN: 9783319786797
NFMCP@PKDD/ECML
NFMCP@PKDD/ECML
This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4479a5d0133845692516645cbdd86d51
https://doi.org/10.1007/978-3-319-78680-3_9
https://doi.org/10.1007/978-3-319-78680-3_9
Autor:
Peter Laurinec, Mária Lucká
Publikováno v:
2017 IEEE 14th International Scientific Conference on Informatics.
This paper presents a new method for forecasting the load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Befo
Publikováno v:
2017 IEEE 14th International Scientific Conference on Informatics.
Ensuring sustainability demands more precise energy management to minimize energy wastage. With the deployment of smart grids that provide a huge amount of data, new methods of machine learning come to light to ensure more precise predictions. With t
Autor:
Peter Laurinec, Petra Vrablecová, Viera Rozinajová, Mária Lucká, Marek Loderer, Anna Bou Ezzeddine
Publikováno v:
ICDM Workshops
The paper presents an improvement of incremental adaptive power load forecasting methods by performing cluster analysis prior to forecasts. For clustering the centroid based method K-means, with K-means++ centroids initialization, was used. Ten vario
Autor:
Mária Lucká, Peter Lacko, Peter Laurinec, Petra Vrablecová, Marek Loderer, Anna Bou Ezzedine, Gabriela Grmanová, Viera Rozinajová
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319273990
NaBIC
NaBIC
Ensemble learning is one of the machine learning approaches, which can be described as the process of combining diverse models to solve a particular computational intelligence problem. We can find the analogy to this approach in human behavior (e.g.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7a797053f9d865816677173d1b005c8
https://doi.org/10.1007/978-3-319-27400-3_21
https://doi.org/10.1007/978-3-319-27400-3_21