摘要
针对电价变化模式的复杂性,提出了一种基于聚类分析的电价预测模型。该模型将复杂的电价预测问题分解为更简单的子问题求解,首先通过聚类技术将输入空间划分为若干特征更明显的子空间,然后在子空间内分别使用支持向量机进行建模和预测。聚类分析中先应用减聚类算法自动确定聚类数并获取较优的初始聚类中心,然后采用K-均值算法进一步优化。采用美国PJM电力市场历史边际电价数据进行的仿真研究表明,电价预测模型能有效、稳定地提高电价预测精度。
A new model of electricity price forecasting based on cluster analysis is proposed. The complex forecasting problem is divided into simpler problems in the presented model. The whole input space is partitioned into several disjointed regions. Then, support vector machine is used for modeling and forecasting for each region. In the process of cluster analysis, K-means algorithm is used for further optimizing after the number of partitioned regions and initial cluster centers are automatically obtained by using subtractive clustering method. The simulation research using the historical data from PJM market shows that the proposed model can improve the precision of electricity price forecasting effectively and stably.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2007年第6期1278-1281,共4页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金项目(70671042)
关键词
聚类
电价
预测方法
电力市场
支持向量机
clustering
electricity price
forecasting method
power market
support vector machine
