Short-Term Load Forecasting Using Support Vector Regression Based on Pattern-Base

• Main Authors: Ying-Chun Guo, Dong-Xiao Niu
• Format: Conference Proceeding
• Language: English
• Subjects: classification and regression tree; Classification tree analysis; Data mining; Load forecasting; Pattern matching; Pattern recognition; Pattern-base; Predictive models; Regression tree analysis; Short-term load forecasting; Support vector regression; Technology forecasting; Training data; Weather forecasting
• DOI: 10.1109/ACIIDS.2009.52

A new idea is proposed that preprocessing is the key to improving the precision of short-term load forecasting (STLF). This paper presents a new model of STLF which is using support vector regression (SVR) based on pattern-base. Our model can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree (CART); secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar features; thirdly, it establishes SVR forecasting model based on the pattern-base which matches to the forecasting day. Since the patterns of daily load are treated beforehand, the rule of the historical data sequence is more obvious. The model has many advantages: first, since the training data has similar pattern to the forecasting day, the model reflects the rule of daily load accurately and improves forecasting precision accordingly; second, as the pattern variables need not to be input into model, the mapping of the categorical variables is solved; third, as inputs are reduced, the model is simplified and the runtime is lessened. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.