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Machine learning methods are main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey...
This paper presents the results of a temperature-load sensitivity study carried out at the Midcontinent Independent System Operator (MISO) for adjusting MISO day-ahead load forecast. As the area operated by MISO is growing rapidly, operators at MISO have to examine the day-ahead load forecast results generated by commercial software packages on a daily basis and manually correct anticipated discrepancies...
Support vector machine (SVM) is based on the statistical learning theory. It has recently been successfully used to solve nonlinear regression and time series problems and has been applied to predict values. The key problem of SVM is the choice of SVM parameters. Particle swarm optimization (PSO) algorithm has the ability of global optimization. This paper proposed an improved PSO algorithm based...
Support Vector Machine (SVM) is a type of learning machine which has been proved to be available in solving the problems of nonlinear regression. The decision of SVM parameters is essential. In this paper a new SVM model based on particle swarm optimization (PSO) for parameter optimization has been proposed. PSO algorithm has extensive capability of global optimization. Once the PSO finds the optimal...
Electric power system load forecasting plays an important role in the energy management system (EMS), which has great effect on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will lead to economic cost saving and right decisions on generating electric power. Electric power load is difficult to be forecasted accurately for...
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