Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia.
This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. Keywords: Computational Intelligence Heterogeneous Regressions Algorithms Performance Evaluation Hybrid Method Mean Absolute Scaled Error (MASE).Ĭomputational 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). Power Engineering Research Group (PERG), Central Queensland University, Rockhampton, Australia.Įmail: November 23 rd, 2012 revised December 23 rd, 2012 accepted December 30 th, 2012
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