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Important variables in explaining real-time peak price in the independent power market of Ontario

机译：解释安大略独立电力市场实时峰值价格的重要变量

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This paper uses support vector machines (SVM) based learning algorithm to select important variables that help explain the realtime peak electricity price in the Ontario market. The Ontario market was opened to competition only in May 2002. Due to the limited number of observations available, finding a set of variables that can explain the independent power market of Ontario (IMO) real-time peak price is a significant challenge for the traders and analysts. The kernel regressions of the explanatory variables on the IMO real-time average peak price show that non-linear dependencies exist between the explanatory variables and the IMO price. This non-linear relationship combined with the low variable-observation ratio rule out conventional statistical analysis. Hence, we use an alternative machine learning technique to find the important explanatory variables for the IMO real-time average peak price. SVM sensitivity analysis based results find that the IMO's predispatch average peak price, the actual import peak volume, the peak load of the Ontario market and the net available supply after accounting for load (energy excess) are some of the most important variables in explaining the real-time average peak price in the Ontario electricity market.
机译：本文使用基于支持向量机（SVM）的学习算法来选择重要变量，以帮助解释安大略市场的实时峰值电价。安大略市场仅在2002年5月才开放竞争。由于可用的观察数有限，寻找一组可以解释安大略独立电力市场（IMO）实时峰值价格的变量对交易员来说是一项重大挑战。和分析师。对IMO实时平均峰值价格的解释变量的核回归表明，解释变量与IMO价格之间存在非线性相关性。这种非线性关系与低可变观测比相结合，排除了常规的统计分析。因此，我们使用替代的机器学习技术来查找IMO实时平均峰值价格的重要解释变量。基于SVM敏感性分析的结果发现，IMO的发货前平均峰值价格，实际进口峰值量，安大略省市场的峰值负荷以及在扣除负荷（能源过剩）之后的可用净供应量是解释以下因素的最重要变量：安大略省电力市场的实时平均峰值电价。

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