Wind profile prediction using linear Markov chains: A linear algebra approach
To predict the future wind speed and wind direction is of relevance to the wind industry to maximize the power generation. In this regards, this article describes a methodology for the construction of predictive models based on linear Markov chains under linear algebra point of view. The model analy...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/115139 |
| Acceso en línea: | https://hdl.handle.net/2117/115139 https://dx.doi.org/10.1109/TLA.2018.8327410 |
| Access Level: | acceso abierto |
| Palabra clave: | Eigenvectors Markov processes MATLAB Markov Chains eigenvectors eigenvalues stationary distribution Espais vectorials Markov, Processos de Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Sumario: | To predict the future wind speed and wind direction is of relevance to the wind industry to maximize the power generation. In this regards, this article describes a methodology for the construction of predictive models based on linear Markov chains under linear algebra point of view. The model analyzes the direction and speed of the wind obtained from a meteorological station. This Model allows making a precise study of wind direction and speeding data; figure out the stability, the most common direction or speed, its behaviour depending on the hours or seasons. |
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