Capítulo de Libro
Time-Series Prediction: Application to the Short-Term Electric Energy Demand
Autor/es | Troncoso Lora, Alicia
Riquelme Santos, Jesús Manuel Riquelme Santos, José Cristóbal Gómez Expósito, Antonio Martínez Ramos, José Luis |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Ingeniería Eléctrica |
Fecha de publicación | 2003 |
Fecha de depósito | 2016-04-01 |
Publicado en |
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Resumen | This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed ... This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and minimum forecasting errors. |
Cita | Troncoso Lora, A., Riquelme Santos, J.M.,...,Martínez Ramos, J.L. (2003). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. En Current Topics in Artificial Intelligence, Lecture Notes in Computer Science, Volume 3040, pp 577-586 (2003) . |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Time series.pdf | 197.7Kb | [PDF] | Ver/ | |