원문정보
초록
영어
In this paper, a new model, core vector regression (CVR) optimized by memetic algorithm (MA), is presented to predict electric daily load. Support vector regression (SVR) has obtained wide focus in recent years to solve nonlinear regression problems in many fields. However, it is limited on large scale dataset problem because of its high time and space complexity. Hence, CVR is proposed to improve the SVR on solving large scale dataset problem. Proper parameters selection of CVR model determines the complexity and accuracy of the model. In this paper, MA is proposed to optimize the parameters of CVR, which is called MA-CVR. Electric load is the time-dependent data which shows recurrent pattern weekly, seasonally and yearly. In this paper, we adopt MA optimization method and choose adaptive parameters dynamically based on time recurrent character of electric load data. Experimental results show that MA-CVR outperforms the existing model optimized by genetic algorithm which is called GA-CVR.
목차
1. Introduction
2. Problem Statement
3. The Proposed Algorithm
3.1. Core Vector Regressions
3.2. Memetic Algorithm
3.3. MA-CVR Model
4. Case Studies
4.1. Historical Data
4.2. Construction of Datasets And Samples
4.3. Model Parameters
4.4 Evaluation Method
4.5 Results
5. Conclusion
Acknowledgements
References