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Abstract
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| The purpose of this paper is electricity price forecasting through regression tree methods. Electricity price forecasting has become the focus of considerable interest in a deregulated energy market. It is essential for suppliers and buyers to rely on reasonable forecasts for the elaboration of bidding strategies to mitigate losses and increase benefits. Here, three forms of regression tree models have been applied: CART, Bagging and Random Forests, which have been used in two directions: first, to identify the variables dominating the marginal price of the commodity and second, for short-run (one hour ahead and day ahead) electricity price forecasting for the Spanish-Iberian Market. | |
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International
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Si |
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Congress
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7Th International Conference of the ERCIM WG on Computational & Methodological Statistics ( ERCIM 2014) |
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960 |
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Place
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Pisa |
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Reviewers
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Si |
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ISBN/ISSN
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978-84-937822-4-5 |
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Start Date
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05/12/2014 |
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End Date
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08/12/2014 |
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From page
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1 |
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To page
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12 |
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Conference Proceedings of the ERCIM WG |