scholarly journals Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power

2017 ◽  
Vol 5 (3) ◽  
pp. 1-14
Author(s):  
Miltiadis Alamaniotis ◽  
Georgios Karagiannis
Keyword(s):  
Particle Swarm Optimization ◽  
Renewable Energy ◽  
Wind Speed ◽  
Gaussian Process ◽  
Wind Farm ◽  
Particle Swarm ◽  
Critical Issue ◽  
Short Term ◽  
Smart Power ◽  
Swarm Optimization

This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Wind Speed Forecasting Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2014 ◽  
Vol 602-605 ◽  
pp. 3251-3255
Author(s):  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Least Squares ◽  
Wind Farm ◽  
Particle Swarm ◽  
Support Vector ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Machine Theory

This paper is based on Least Squares Support Vector Machine theory to build the wind speed forecasting model. Meanwhile, as there is still no effective choice method of Least Squares Support Vector Ma-chine parameter, this paper tried to use Particle Swarm Optimization theory to optimization choice for parameter. And last, use wind farm observed wind speed (sampling interval is 1 minute) of three days to forecast the next minute wind speed through this paper's wind forecasting model, and prediction result is that the MAPE is only 4.63%, the prediction effect is relative ideal, confirm the feasibility of applying the Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine theory to forecast the wind speed, it will provide theoretical support to wind farm layout and wind power forecasting and so on.

scholarly journals Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongqiang Wu ◽  
Wenjing Jia ◽  
Liru Zhao ◽  
Changhan Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Prediction Accuracy ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization ◽  
B Spline ◽  
Input Space ◽  
Definition Of

Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed. The B-spline neural network can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple, and the convergence speed is fast, but it is easy to fall into local minimum. The traditional method to divide the input space is thoughtless and it will influence the final prediction accuracy. Particle swarm optimization is adopted to solve the problem by optimizing the nodes. Simulated results show that it has higher prediction accuracy than traditional B-spline neural network and BP neural network.

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