LMP Forecasting with Prefiltered Gaussian Process
LMP Forecasting with Prefiltered Gaussian Process
复制标题
使用预过滤高斯过程进行末次月经预测
DOI：
10.1109/ciasg.2014.7011555
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
H. Mori and K. Nakano
中科院分区：
文献类型：
--
作者：
H. Mori and K. Nakano
提出了一种新的智能电网负荷边际电价预测方法。在平衡电力系统中，当某一节点的电力需求增加时，需要边际成本来向增量负荷供电。在电力市场环境下，LMP可以使电力从低成本地区流向高成本地区，缓解输电网阻塞，对维持经济效率具有重要作用。电力市场参与者通过售电和购电来实现利润最大化和风险最小化。因此，提前获得准确的电价预测信息，以反映他们的愿望是非常重要的。本文提出了高斯过程（GP）技术，它来自支持向量机（SVM）的扩展，引入分层贝叶斯估计来表示模型参数的概率变量。其优点是GP的模型精度优于其他方法。在本文中，GP与聚类的k-means方法相结合，以提高GP的性能。另外，本文利用广义规划中的马氏核而不是高斯核，从而将广义规划推广到非线性系统的逼近。该方法成功地应用于美国ISO新英格兰地区的真实的数据。
In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion in alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) that hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. In this paper, GP is integrated with the k-means method of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. The proposed method is successfully applied to real data of ISO New England in USA.