论文信息 - Electricity Consumption Prediction Based on SVR with Ant Colony Optimization

Electricity Consumption Prediction Based on SVR with Ant Colony Optimization

Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate. The SVR model with ant colony optimization is proposed according to the characteristics of the nonlinear electricity consumption data. Then ACO-SVR model is applied to the electricity consumption prediction of Jiangsu province. The result shows better than the ANNs method and improves the accuracy of the prediction. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3557 Full Text: PDF

Haijiang Wang | Shanlin Yang | Shanlin Yang | Haijiang Wang

[1] Farshid Keynia,et al. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems , 2011 .

[2] Diyar Akay,et al. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[3] M. Barker,et al. Partial least squares for discrimination , 2003 .

[4] Kadir Kavaklioglu,et al. Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression , 2011 .

[5] Jin Kwak,et al. Mutual Authentication and Key establishment mechanism using DCU certificate in Smart Grid , 2012 .

[6] Wei-Chiang Hong,et al. Electric load forecasting by support vector model , 2009 .

[7] A. Goia,et al. Functional clustering and linear regression for peak load forecasting , 2010 .

[8] Kyoung-jae Kim,et al. Financial time series forecasting using support vector machines , 2003, Neurocomputing.