Open AccessSupport Vector Machine based on clustering algorithm for interruptible load forecasting
Author(s) -
Xi Yu,
G. Bu,
Bingyue Peng,
Chen Zhang,
Xiaolan Yang,
Jun Wu,
Wenqing Ruan,
Yu Yu,
Liangcai Tang,
Ziqing Zou
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/533/1/012018
Subject(s) - cluster analysis , support vector machine , computer science , data mining , scheduling (production processes) , algorithm , machine learning , artificial intelligence , mathematical optimization , mathematics
Accurately forecast interruptible load can help to alleviate the power supply tension during peak load and make scheduling more flexible. Support vector machine (SVM) method which has been widely used in load forecasting usually selects a period of date close to the forecast day without considering the information characteristics of itself. An interruptible load forecasting method based on clustering algorithm is proposed in this paper. This method puts forward a new idea to select the sample of prediction model which takes full account of the weather and date information of the forecast day and solve the problem that the traditional SVM method cannot properly reflect it. In this paper, the principles of clustering algorithm and support vector machine are introduced firstly. Then K-means clustering algorithm is used to classify the historical data, and the support vector machine forecasting model is constructed by using the categories of the forecast day membership. Finally, the prediction is carried out by combining with the actual data. The results show that the prediction accuracy of this method is more than 95%, and it has higher precision.