Collaborative wind power forecast

Conference paper


Gomes de Almeida, V. and Gama, J. 2014. Collaborative wind power forecast. Bouchachia, H. (ed.) Third International Conference, ICAIS 2014. Bournemouth, UK 08 - 10 Sep 2014 Cham, Switzerland Springer. pp. 162-171 https://doi.org/10.1007/978-3-319-11298-5_17
TypeConference paper
TitleCollaborative wind power forecast
AuthorsGomes de Almeida, V. and Gama, J.
Abstract

There are several new emerging environments, generating data spatially spread and interrelated. These applications reinforce the importance of the development of analytical systems capable to sense the environment and receive data from different locations. In this study we explore collaborative methodologies in a real-world problem: wind power prediction. Wind power is considered one of the most rapidly growing sources of electricity generation all over the world. The problem consists of monitoring a network of wind farms that collaborate by sharing information in a very short-term forecasting problem. We use an auto-regressive integrated moving average (ARIMA) model. The Symbolic Aggregate Approximation (SAX) is used in the selection of the set of neighbours. We propose two collaborative methods. The first one, based on a centralized management, exchange data-points between nodes. In the second approach, correlated wind farms share their own ARIMA models. In the experimental work we use 1 year data from 16 wind farms. The goal is to predict the energy produced at each farm every hour in the next 6 hours. We compare the proposed methods against ARIMA models trained with data of each one of the farms and with the persistence model at each farm. We observe a small but consistent reduction of the root mean square error (RMSE) of the predictions.

KeywordsWind Power; Time Series Analysis; Collaborative Forecast; Correlation; Arima
ConferenceThird International Conference, ICAIS 2014
Page range162-171
Proceedings TitleAdaptive and Intelligent Systems:Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-9, 2014. Proceedings
SeriesLecture Notes in Computer Science
EditorsBouchachia, H.
ISSN0302-9743
Electronic1611-3349
ISBN
Hardcover9783319112978
Electronic9783319112985
PublisherSpringer
Place of publicationCham, Switzerland
Publication dates
Online13 Aug 2014
Print22 Sep 2014
Publication process dates
Deposited05 Mar 2018
Accepted06 Jul 2014
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-11298-5_17
Web of Science identifierWOS:000346932400017
Web address (URL) of conference proceedingshttps://doi.org/10.1007/978-3-319-11298-5
LanguageEnglish
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