A Comparison of Two Techniques for Next-Day Electricity Price Forecasting

Authors:
Alicia Troncoso Lora;Jesús Riquelme Santos;José Cristóbal Riquelme Santos;Antonio Gómez Expósito;José Luís Martínez Ramos
Affiliations:
-;-;-;-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

Citing 1
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Design of specific-to-problem kernels and use of kernel weighted K-nearest neighbours for time series modelling

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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.