Capítulo de Libro
A Comparison of Two Techniques for Next- Day Electricity Price Forecasting
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 | 2002 |
Fecha de depósito | 2016-03-30 |
Publicado en |
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Resumen | In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a ... In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported. |
Cita | Troncoso Lora, A., Riquelme Santos, J.M.,...,Martínez Ramos, J.L. (2002). A Comparison of Two Techniques for Next- Day Electricity Price Forecasting. En Intelligent Data Engineering and Automated Learning — IDEAL 2002, Lecture Notes in Computer Science, Volume 2412, pp 384-390 (2002) . |
Ficheros | Tamaño | Formato | Ver | Descripción |
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A comparison of two.pdf | 156.6Kb | [PDF] | Ver/ | |