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

Alamaniotis, Miltiadis and Karagiannis, Georgios (2019) 'Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation.', in 2019 IEEE Milan PowerTech. .

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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/PTC.2019.8810415
Publisher statement:© 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.
Date accepted:26 August 2019
Date deposited:18 January 2021
Date of first online publication:26 August 2019
Date first made open access:18 January 2021

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