A solar radiation intelligent forecasting framework based on feature selection and multivariable fuzzy time series

Abstract

Accurate solar radiation forecasting can effectively improve solar energy utilization efficiency and decrease the operational cost of solar photovoltaic power plants. However, some common forecasting methods have certain limitations, such as neglecting data fuzziness, meteorological factors, feature selection, and seasonal adjustment. Therefore, a solar radiation intelligent forecasting framework based on feature selection and multivariable fuzzy time series is proposed. Specifically, a combined fuzzy strategy is used to fuzzy the data, and an improved multi-objective optimization algorithm is proposed to search for the optimal parameters. A seasonal multivariable fuzzy time series is proposed to achieve multivariable inputs and seasonal adjustments. The experimental analysis, statistical test, and robustness analysis all verify the superiority of the proposed forecasting framework compared with competitive models. The MAPE values of the proposed forecasting framework for two regions are about 6% and 4%, respectively, which outperform some common basic models such as BPNN(about 11% and 9%), ELM(about 12% and 10%), LSTM(about 9% and 7%). The comparison analysis further indicates that the vital parts of the forecasting framework containing feature selection, seasonal adjustment, multi-objective optimization, and multivariable inputs can all have a positive influence on forecasting performance.