combination of transformed-means clustering and neural networks for short-term solar radiation forecasting

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.