Collaborative Wind Power Forecast

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.

[1]  Vladimiro Miranda,et al.  A quick guide to wind power forecating : state-of-the-art 2009. , 2009 .

[2]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[3]  Eamonn J. Keogh,et al.  Probabilistic discovery of time series motifs , 2003, KDD '03.

[4]  Laurent Dubus,et al.  Analog Method for Collaborative Very-Short-Term Forecasting of Power Generation from Photovoltaic Systems , 2011 .

[5]  João Gama,et al.  Ubiquitous Knowledge Discovery , 2011, IDA 2011.

[6]  Kristin Larson,et al.  Short-term wind forecasting using off-site observations , 2006 .

[7]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[8]  A. Appice,et al.  The Intelligent Forecasting Model of Time Series , 2013 .

[9]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[10]  C. Guestrin,et al.  Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[11]  Ya-Ju Fan,et al.  Finding Motifs in Wind Generation Time Series Data , 2012, 2012 11th International Conference on Machine Learning and Applications.

[12]  Vladimiro Miranda,et al.  Wind power forecasting : state-of-the-art 2009. , 2009 .

[13]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[14]  Peng Guo,et al.  A Review of Wind Power Forecasting Models , 2011 .

[15]  José de Jesús Rubio,et al.  Analytic neural network model of a wind turbine , 2015, Soft Comput..

[16]  Lorenza Saitta,et al.  Introduction: The Challenge of Ubiquitous Knowledge Discovery , 2010, Ubiquitous Knowledge Discovery.