Time-Series Prediction: Application to the Short-Term Electric Energy Demand
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...
| Autores: | , , , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39336 |
| Acceso en línea: | http://hdl.handle.net/11441/39336 https://doi.org/10.1007/978-3-540-25945-9_57 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial Intelligence (incl. Robotics) Mathematical Logic and Formal Languages Computation by Abstract Devices |
| Sumario: | 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. |
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