Daily Load Forecasting Using Support Vector Machine and Case-Based Reasoning

摘要：

Regarding to the daily load forecasting, the sample selection and data preprocessing are crucial to its precision. In this paper, case-based reasoning (CBR) is adopted to search the historical data whose features are the same as the predict day. CBR is realized through the steps of case representation, indexing, retrieval, and adaptation, and the key idea in CBR involves the use of already existing knowledge about objects or situations to predict aspects of similar objects. This method uses not only case specific knowledge of past problems, but also uses additional knowledge derived from the clusters of cases. After the data pretreated the sample set becomes more relational with the predict day. Meanwhile the training sample set for support vector machine (SVM) for daily load forecasting (DLF) becomes smaller. With the prediction precision increasing, the time for calculating and predicting decreased. At last, the testing results on a real power system show that the proposed model is feasible and effective for load forecasting.

作者: Dongxiao NIU Jinchao LI Jinying LI Qiang WANG

作者单位: North China Electric Power University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）