Multi-agent architecture for optimal energy management of a smart micro-grid using a weighted hybrid BP-PSO algorithm for wind power prediction

2016 ◽  
Vol 11 (1) ◽  
pp. 20 ◽  
Author(s):  
Didi Omar Elamine ◽  
Maria Serraji ◽  
El Habib Nfaoui ◽  
Jaouad Boumhidi
Keyword(s):  
Wind Power ◽  
Energy Management ◽  
Pso Algorithm ◽  
Agent Architecture ◽  
Power Prediction ◽  
Optimal Energy ◽  
Wind Power Prediction ◽  
Multi Agent ◽  
Optimal Energy Management ◽  
Micro Grid

Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

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