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

 

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Opened Access A Comparison of Two Techniques for Next- Day Electricity Price Forecasting
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Author: Troncoso Lora, Alicia
Riquelme Santos, Jesús Manuel
Riquelme Santos, José Cristóbal
Gómez Expósito, Antonio
Martínez Ramos, José Luis
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2002
Published in: Intelligent Data Engineering and Automated Learning — IDEAL 2002, Lecture Notes in Computer Science, Volume 2412, pp 384-390 (2002)
Document type: Chapter of Book
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.
Size: 156.6Kb
Format: PDF

URI: http://hdl.handle.net/11441/39155

DOI: http://dx.doi.org/10.1007/3-540-45675-9_57

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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