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				<title><![CDATA[&quot;Serving the energy market&quot; - Articles - ]]></title>
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					  <title><![CDATA[Electricity price forecasting through transfer function models]]></title>
					  <link>http://www.erasmusenergy.com/articles/101/1/Electricity-price-forecasting-through-transfer-function-models/Page1.html</link>
					  <description><![CDATA[Keywords (max 10): forecasting; electricity markets; time-series analysis<br/>Published in: Journal of the Operational Research Society<br/>Production / Publication year: 2006<br/>Co-author 1: Conejo, Antonio J<br/><br/>Forecasting electricity prices in nowadays competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes different transfer function models to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build them. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The models proposed are compared with na&iuml;ve and other techniques.<br/>]]></description>
					  <author>no@spam.com (Francisco J Nogales)</author>
					  <pubDate>Thu, 15 Nov 2007 18:28:01 CET</pubDate>
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					  <title><![CDATA[Forecasting Next-Day Electricity Prices by Time Series Models]]></title>
					  <link>http://www.erasmusenergy.com/articles/61/1/Forecasting-Next-Day-Electricity-Prices-by-Time-Series-Models/Page1.html</link>
					  <description><![CDATA[Keywords: Electricity markets, forecasting, market clearing<br/>price, time series analysis.<br/>Published in: IEEE TRANSACTIONS ON POWER SYSTEMS<br/>Publication year: 2002<br/>Co-author 1: Javier Contreras<br/>Co-author 2: Antonio J. Conejo<br/>Co-author 3: Rosario Espinola<br/><br/>In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.]]></description>
					  <author>no@spam.com (Francisco J Nogales)</author>
					  <pubDate>Mon, 01 Oct 2007 12:13:10 CEST</pubDate>
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