Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.
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