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

Alamaniotis, M.; Karagiannis, G.

Abstract

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.

Citation

Alamaniotis, M., & Karagiannis, G. (2017). Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research, 5(3), 1-14. https://doi.org/10.4018/ijmstr.2017070101

• Journal Article Type: Article
• Acceptance Date: Dec 6, 2017
• Publication Date: Jul 1, 2017
• Deposit Date: Dec 7, 2017
• Publicly Available Date: May 11, 2018
• Journal: International Journal of Monitoring and Surveillance Technologies Research
• Print ISSN: 2166-7241
• Electronic ISSN: 2166-725X
• Publisher: IGI Global
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 3
• Pages: 1-14
• DOI: https://doi.org/10.4018/ijmstr.2017070101
• Public URL: https://durham-repository.worktribe.com/output/1370019