<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>DSpace Collection:</title>
    <link>http://hdl.handle.net/2117/5939</link>
    <description />
    <pubDate>Mon, 20 May 2013 00:41:54 GMT</pubDate>
    <dc:date>2013-05-20T00:41:54Z</dc:date>
    <itunes:owner>
      <itunes:email>webmaster.bupc@upc.edu</itunes:email>
      <itunes:name>Universitat Politècnica de Catalunya. Servei de Biblioteques i Documentació</itunes:name>
    </itunes:owner>
    <itunes:explicit>no</itunes:explicit>
    <itunes:keywords />
    <item>
      <title>Improving electricity market price scenarios by means of forecasting factor models</title>
      <link>http://hdl.handle.net/2117/3047</link>
      <description>Title: Improving electricity market price scenarios by means of forecasting factor models
Authors: Muñoz Gracia, María del Pilar; Corchero García, Cristina; Heredia, F.-Javier (Francisco Javier)
Abstract: In liberalized electricity markets, generation Companies must build an hourly bid&#xD;
that is sent to the market operator. The price at which the energy will be paid is unknown during the bidding process and has to be forecast. In this work we apply forecasting factor models to this framework and study its suitability.</description>
      <pubDate>Tue, 01 Sep 2009 11:47:42 GMT</pubDate>
      <guid isPermaLink="false">http://hdl.handle.net/2117/3047</guid>
      <dc:date>2009-09-01T11:47:42Z</dc:date>
      <itunes:author>Muñoz Gracia, María del Pilar; Corchero García, Cristina; Heredia, F.-Javier (Francisco Javier)</itunes:author>
      <itunes:explicit>no</itunes:explicit>
      <itunes:keywords>Electricity market prices, Short term forecasting, Factor models, Price scenarios</itunes:keywords>
      <itunes:summary>In liberalized electricity markets, generation Companies must build an hourly bid&#xD;
that is sent to the market operator. The price at which the energy will be paid is unknown during the bidding process and has to be forecast. In this work we apply forecasting factor models to this framework and study its suitability.</itunes:summary>
    </item>
  </channel>
</rss>

