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Price forecast in the competitive electricity market by support vector machine

机译:支持向量机在竞争性电力市场中的价格预测

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摘要

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter Ci and ei assigned to each training data, the flexible Ci Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin R-m, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS). (C) 2007 Elsevier B.V. All rights reserved.
机译:电力市场已在世界许多国家广泛引入,对电价预测技术的研究引起了很多关注。在本文中,为每个训练数据分配了不同的参数Ci和ei,根据电力市场价格预测的特殊性,开发了灵活的Ci支持向量回归(SVR)模型。对于日前市场(DAM)价格预测,负荷,使用时间指数和日类型指数是表征市场价格的主要因素,因此,它们被选择作为灵活SVR预测模型的输入。对于长期价格预测,我们将储备裕度R-m,HHI和燃油价格指数作为输入,因为它们是长期驱动市场价格变动的主要因素。对于短期价格预测,除了对年轻的意大利电力市场进行详细分析之外,新模型还将在西班牙市场,纽约市场和新英格兰市场的试验阶段进行测试。使用长期市场模拟器(LREMS)的数据进行的预测证明了采用SVR模型进行的长期预测的合理性。 (C)2007 Elsevier B.V.保留所有权利。

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