Capítulo de Libro
Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production
Autor/es | Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal Martínez Ramos, José Luis Riquelme Santos, Jesús Manuel Gómez Expósito, Antonio |
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 presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique ... This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied. |
Cita | Troncoso Lora, A., Riquelme Santos, J.C.,...,Gómez Expósito, A. (2003). Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production. En Progress in Artificial Intelligence, Lecture Notes in Computer Science, Volume 2902, pp 189-203 (2003) . |
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