Journal
APPLIED SOFT COMPUTING
Volume 48, Issue -, Pages 207-216Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2016.07.022
Keywords
Clustering; Data preprocessing; Forecasting; Soft computing; Solar radiation; TSC K-means
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Accurate forecasting of renewable-energy sources plays a key role in their integration into the grid. This paper proposes a novel soft computing framework using a modified clustering technique, an innovative hourly time-series classification method, a new cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to increase the solar radiation forecasting accuracy. The proposed clustering method is an improved version of K-means algorithm that provides more reliable results than the K-means algorithm. The time series classification method is specifically designed for solar data to better characterize its irregularities and variations. Several different solar radiation datasets for different states of U.S. are used to evaluate the performance of the proposed forecasting model. The proposed forecasting method is also compared with the existing state-of-the-art techniques. The comparison results show the higher accuracy performance of the proposed model. (C) 2016 Elsevier B.V. All rights reserved.
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