A novel soft computing framework for solar radiation forecasting

作者：Ghofrani Mahmoud^*; Ghayekhloo Mohadeseh; Azimi Rasool

来源：Applied Soft Computing, 2016, 48: 207-216.

DOI：10.1016/j.asoc.2016.07.022

• Clustering
• Data preprocessing
• Forecasting
• Soft computing
• Solar radiation
• TSC K-means

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.

• 出版日期2016-11