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Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation

Alamaniotis, Miltiadis; Karagiannis, Georgios

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Authors

Miltiadis Alamaniotis



Abstract

This paper presents an intelligent data driven method for forecasting minute ahead wind speed, which is essential in predicting the power output coming from wind generators. The proposed methodology, is based on the principle that “the most recent past should be used to predict the near future”, and implements a two-stage forecasting method. In the first stage a Gaussian Process Regression model is trained multiple times on different length time window, and forecasts a set of next minute wind speed values. In the second stage, a fuzzy inference system collects the forecasts, rejects some of them and then provides a mean and a variance of a single forecast value. The proposed method is applied to a dataset of real-world data, and benchmarked against the autoregression (AR) model. Results exhibit the superiority of the proposed method over AR as well as over GPR which uses a single train set.

Citation

Alamaniotis, M., & Karagiannis, G. (2019, December). Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation. Presented at 2019 IEEE Milan PowerTech

Presentation Conference Type Conference Paper (published)
Conference Name 2019 IEEE Milan PowerTech
Acceptance Date Aug 26, 2019
Online Publication Date Aug 26, 2019
Publication Date 2019
Deposit Date Oct 30, 2020
Publicly Available Date Jan 18, 2021
Publisher Institute of Electrical and Electronics Engineers
Book Title 2019 IEEE Milan PowerTech
DOI https://doi.org/10.1109/ptc.2019.8810415
Public URL https://durham-repository.worktribe.com/output/1140049

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Accepted Conference Proceeding (591 Kb)
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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