Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

• Published in: Mathematical problems in engineering 2014-01, Vol.2014 (1)
• Main Authors: Chen, Jiqiang, Xue, Xiaoping, Ha, Minghu, Yu, Daren, Ma, Litao
• Format: Article
• Language: English
• Subjects: Accuracy; Alternative energy sources; Fluctuation; Gene expression; Mathematical analysis; Mathematical models; Mathematical problems; Meteorology; Methods; Neural networks; Predictions; Probability; Probability distribution; Probability theory; Regression; Regression models; Running; Science; Statistical analysis; Support vector machines; Time series; Vectors (mathematics); Wind power; Wind speed
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2014/410489

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction.