A Comparison of Two Techniques for Next- Day Electricity Price Forecasting
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/und...
| Autores: | , , , , |
|---|---|
| Formato: | capítulo de livro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2002 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39155 |
| Acesso em linha: | http://hdl.handle.net/11441/39155 https://doi.org/10.1007/3-540-45675-9_57 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial Intelligence (incl. Robotics) Data Structures Cryptology and Information Theory Information Storage and Retrieval Information Systems Applications (incl. Internet) Pattern Recognition Database Management |
| Resumo: | 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. |
|---|