Prediction of solar radiation with genetic approach combing multi-model framework

In this paper, a genetic approach combing multi-model framework for solar radiation time series prediction is proposed. The framework starts with the assumption that there exists several different patterns in the stochastic component of the solar radiation series. To uncover the underlying pattern, a genetic algorithm is used to segment the solar series dynamically, and the subsequences are further grouped into different clusters. For each cluster, a prediction model is trained to represent that specific pattern. In the prediction phase, identifying the pattern for current period is of great importance. Thus a procedure for the pattern identification is performed to identify the proper pattern for the series belong to. The prediction result of the proposed framework is then compared to other algorithms. It shows that the proposed framework could provide superior performance compared to others.

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