Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

• Main Authors: Qiu Jing, Xu Xiaozhong, Deng Song, Wang Ting
• Format: Article
• Language: English
• Published: Academic Journals Center of Shanghai Normal University 2017-08-01
• Series: Journal of Shanghai Normal University (Natural Sciences)
• Subjects: short term load forecasting; similar days; similarity; fuzzy c-means (FCM) clustering; particle swarm optimization (PSO) algorithm; support vector machine (SVM)
• Online Access: http://qktg.shnu.edu.cn/zrb/shsfqkszrb/ch/reader/view_abstract.aspx?file_no=20170416&flag=1

Traditional fuzzy c-means (FCM) clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO) algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM) algorithms.