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G. Brown, J. Wyatt and P. Tino, “Managing Diversity in Regression Ensembles,” Journal of Machine Learning Research, Vol. 6, No. 1, 2005, pp. 1621-1650.

has been cited by the following article:

• TITLE: Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

  AUTHORS: Md Rahat Hossain, Amanullah Maung Than Oo, A. B. M. Shawkat Ali

  KEYWORDS: Computational Intelligence; Heterogeneous Regressions Algorithms; Performance Evaluation; Hybrid Method; Mean Absolute Scaled Error (MASE)

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.4 No.1, February 26, 2013

  ABSTRACT: Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.