Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting

Document Type : Research Article


1 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran


In order to provide an efficient conversion and utilization of solar power, solar radiation data
should be measured continuously and accurately over the long-term period. However, the measurement of
solar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,
several studies were proposed in the literature to find mathematical and physical models to estimate and
forecast the amount of solar radiation such as stochastic prediction models based on time series methods. This
paper proposes a hybridization framework, considering clustering, pre-processing, and training steps for shortterm
solar radiation forecasting. The proposed method is a combination of a novel data clustering method,
time-series analysis, and multilayer perceptron neural network (MLPNN). The proposed Transformed-
Means clustering method is based on inverse data transformation and K-means algorithm that presents more
accurate clustering results when compared to the K-Means algorithm; its improved version and also other
popular clustering algorithms. The performance of the proposed Transformed-Means is evaluated using
several types of datasets and compared with different variants of K-means algorithm. The proposed method
clusters the input solar radiation time-series data into an appropriate number of sub-datasets which are then
preprocessed by the time-series analysis. The preprocessed time-series data provide the input for the training
stage where MLPNN is used to forecast the solar radiation. Solar time-series data with different solar radiation
characteristics are also used to determine the accuracy and the processing speed of the developed forecasting
method with the proposed Transformed-Means and other clustering techniques.


Main Subjects

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