Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production
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 co...
| Autores: | , , , , |
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| Formato: | capítulo de livro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39326 |
| Acesso em linha: | http://hdl.handle.net/11441/39326 https://doi.org/10.1007/978-3-540-24580-3_26 |
| Access Level: | acceso abierto |
| Palavra-chave: | Nearest neighbours load forecasting optimal energy production |
| Resumo: | 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. |
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