Collaborative wind power forecast

Gomes de Almeida, Vania ORCID logoORCID: and Gama, João (2014) Collaborative wind power forecast. Bouchachia, Hamid, ed. Adaptive and Intelligent Systems. In: Third International Conference, ICAIS 2014, 08-10 Sept 2014, Bournemouth, UK. ISBN 9783319112978, e-ISBN 9783319112985. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-11298-5_17)


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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 23728
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Depositing User: Vania Gomes De almeida
Date Deposited: 05 Mar 2018 17:47
Last Modified: 20 Aug 2020 12:22

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