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PLS-SVR Optimized by PSO Algorithm for Electricity Consumption Forecasting |
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PP: 331-338 |
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Author(s) |
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Zhiqiang Chen,
Shanlin Yang,
Xiaojia Wang,
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Abstract |
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| The development of smart grid and electricity market requires more accurate electricity consumption forecasting. The
impact of different parameters of Support vector regression (SVR) on electricity consumption forecasting, and the parameters of SVR
model were preprocessed through Particle Swarm Optimization (PSO) to get the optimum parameter values. For the input variables
of forecasting model are normalized to reduce the influence of different units on SVR model, and using the partial least square
method (PLS) can solve the multicollinearity between the independent variable. A actual data is employed to simulate computing,
the result shows proposed method could reduce modeling error and forecasting error, and compared with back-propagation artificial
neural networks (BP ANN) and single LS-SVR algorithm, PSO-PLS-SVR algorithm can achieve higher prediction accuracy and better
generalized performance. |
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