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首页> 外文期刊>Generation, Transmission & Distribution, IET >Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests
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Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests

机译：基于回归树模型的重要变量评估和电价预测：分类和回归树，装袋和随机森林

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Electricity price forecasting has become the focus of considerable interest in a deregulated energy market. In this study, regression tree-based models: classification and regression trees, Bagging and Random Forests have been built and used to identify the variables dominating the marginal price of the commodity as well as for short-term (one hour and day ahead) electricity price forecasting for the Spanish–Iberian market. Different prediction models are proposed including the main features of the market such as load, hydro and thermal generation and from available, wind energy production, of strategic interest in the Spanish market. In addition other explanatory variables are considered as lagged prices, as well as hour, day, month and year indicators. In the study, hourly data from 2000–2011 corresponding to 22 variables have been used. The results show the effectiveness of the proposed ensemble of tree-based models which emerge as an alternative and promising tool, competitive with other existing methods.
机译：在放松管制的能源市场中，电价预测已成为人们关注的焦点。在这项研究中，建立了基于回归树的模型：分类和回归树，装袋和随机森林，并用于识别主导商品边际价格以及短期（提前一天一小时）用电的变量西班牙-伊比利亚市场的价格预测。提出了不同的预测模型，包括市场的主要特征，例如负荷，水力和热力发电以及来自西班牙市场具有战略意义的可利用的风能生产。此外，其他解释性变量也被视为滞后价格，以及小时，日，月和年指标。在这项研究中，使用了2000-2011年的每小时数据，对应于22个变量。结果表明，所提出的基于树的模型集合的有效性，该树模型作为替代和有希望的工具而出现，与其他现有方法竞争。

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