Collaborative Wind Power Forecast

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.