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Durham Research Online
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Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes

Alamaniotis, M. and Karagiannis, G. (2018) 'Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes.', Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018) Dubrovnik, Croatia, 12-15 Nov 2018.

Abstract

The smart power systems of the future will be able to accommodate wind power at a maximum efficiency by utilizing available information. For instance, information pertained to wind speed is essential in forecasting the overall amount of power generated by wind farms. Information is used to offset the inherent stochasticity of wind power and improve wind speed forecasting precision. In this work, an intelligent methodology for quantifying the uncertainty of wind speed pertained to forecasting is introduced. The introduced methodology adopts a set of Gaussian processes to assemble a model of the uncertainty of the forecasted speed. Results are taken on a set of real-world wind speed data.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1049/cp.2018.1888
Publisher statement:This is a preprint of a chapter accepted by Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018) and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital Library: https://doi.org/10.1049/cp.2018.1888
Date accepted:No date available
Date deposited:12 August 2021
Date of first online publication:2018
Date first made open access:12 August 2021

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