Machine Learning based short term wind power prediction using a hybrid learning model. (July 2015)

• Record Type: Journal Article
• Authors: Najeebullah, ; Zameer, Aneela; Khan, Asifullah; Javed, Syed Gibran
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 122
• Page End: 133
• Publication Date: 2015-07
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.07.009
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Depletion of conventional resources has led to the exploration of renewable energy resources. In this regard, wind power is taking significant importance, worldwide. However, to acquire consistent power generation from wind, the expected wind power is required in advance. Consequently, various prediction models have been reported for wind power prediction. However, we observe that Support Vector Regression (SVR), and specially, a hybrid learning model based on SVR offer better performance and generalization compared to multiple linear regression (MLR) and is thus quite suitable for the development of short-term wind power prediction system. To this end, a new methodology ML-STWP namely Machine Learning based Short Term Wind Power Prediction is proposed for short-term wind power prediction. This approach utilizes a combination of machine learning (ML) techniques for feature selection and regression. The proposed methodology is thus a hybrid ML model, which makes use of feature selection through irrelevancy and redundancy filters, and then employs SVR for auxiliary prediction. Finally, the wind power is predicted using enhanced particle swarm optimization and a hybrid neural network. The wind power dataset on which the model is tuned and tested consists of real-time daily values of wind speed, relative humidity, temperature, and wind power. The obtained results demonstrate that the proposed prediction model performs better as compared to the existing methods and demonstrates the efficacy of the proposed intelligent system in accurately predicting wind power on daily basis.