A soft computing approach to projecting locational marginal price
A soft computing approach to projecting locational marginal price
复制标题
预测位置边际价格的软计算方法
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Fahrioglu
中科院分区：
文献类型：
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作者：
N. Nwulu;M. Fahrioglu
近年来，世界上大多数国家对电力市场的放松管制程度不断加深，电力公司必须设计准确、高效的机制来确定电力系统中的位置边际电价 (LMP)。本文对两种基于软计算的方案进行了比较：用于 LMP 投影的人工神经网络和支持向量机。我们的系统以有用的电力系统参数作为输入，以 LMP 作为输出。获得的实验结果表明，尽管两种方法都给出了高度准确的结果，但支持向量机的性能略优于人工神经网络，并且计算时间成本可控。
The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.