Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms
Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms作者机构：Power Engineering Research Group (PERG) Central Queensland University Rockhampton Australia.
出 版 物：《Smart Grid and Renewable Energy》 (智能电网与可再生能源（英文）)
年 卷 期：2013年第4卷第1期
页 面：76-87页
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Computational Intelligence Heterogeneous Regressions Algorithms Performance Evaluation Hybrid Method Mean Absolute Scaled Error (MASE)
摘 要：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.
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