Abstract
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.
Cite
CITATION STYLE
Lora, A. T., Santos, J. R., Santos, J. R., Expósito, A. G., & Ramos, J. L. M. (2002). A comparison of two techniques for next-day electricity price forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2412, pp. 384–390). Springer Verlag. https://doi.org/10.1007/3-540-45675-9_57
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