- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2196-5420
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Long-term system load forecasting based on data-driven linear clustering method ; day:17 ; month:5 ; year:2017 ; pages:1-11
Journal of modern power systems and clean energy ; (17.5.2017), 1-11
- Classification
-
Elektrotechnik, Elektronik
- Creator
-
LI, Yiyan
- Contributor
-
Han, Dong
Yan, Zheng
SpringerLink (Online service)
- DOI
-
10.1007/s40565-017-0288-x
- URN
-
urn:nbn:de:1111-201705266175
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:37 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- LI, Yiyan
- Han, Dong
- Yan, Zheng
- SpringerLink (Online service)
Other Objects (12)
Ground-level ozone forecasting using data-driven methods
Indicators of economic crises: a data-driven clustering approach
Revenue forecasting in the mining industries: A data-driven approach
Data-driven surrogate model for wind turbine damage equivalent load
New Data-Driven Probabilistic Forecasting Methods with Applications in Energy Systems
Forecasting Chikungunya spread in the Americas via data-driven empirical approaches
Data-driven load profiles and the dynamics of residential electricity consumption
A real data-driven clustering approach for countries based on happiness score
Critical clusters in interdependent economic sectors : A data-driven spectral clustering analysis
Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach
Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
Ideal point error for model assessment in data-driven river flow forecasting
Ground-level ozone forecasting using data-driven methods
Indicators of economic crises: a data-driven clustering approach
Revenue forecasting in the mining industries: A data-driven approach
Data-driven surrogate model for wind turbine damage equivalent load
New Data-Driven Probabilistic Forecasting Methods with Applications in Energy Systems
Forecasting Chikungunya spread in the Americas via data-driven empirical approaches
Data-driven load profiles and the dynamics of residential electricity consumption
A real data-driven clustering approach for countries based on happiness score
Critical clusters in interdependent economic sectors : A data-driven spectral clustering analysis
Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach
Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
Ideal point error for model assessment in data-driven river flow forecasting
Ground-level ozone forecasting using data-driven methods
Indicators of economic crises: a data-driven clustering approach
Revenue forecasting in the mining industries: A data-driven approach
Data-driven surrogate model for wind turbine damage equivalent load
New Data-Driven Probabilistic Forecasting Methods with Applications in Energy Systems
Forecasting Chikungunya spread in the Americas via data-driven empirical approaches
Data-driven load profiles and the dynamics of residential electricity consumption
A real data-driven clustering approach for countries based on happiness score
Critical clusters in interdependent economic sectors : A data-driven spectral clustering analysis
Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach
Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks