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with the increasing of penetration of photovoltaic power, the solar irradiance forecasting has become a concern for power management. Due to the non-stationary and stochastic feature of the solar irradiance in a very time-term scale, the solar irradiance is difficult to accurately forecast. In this paper, the second-level solar irradiance is predicted using a deep-learning-based convolutional neural network (CNN) with the help of wavelet transformation (WT) and bootstrap sampling methods in time-frequency domain. The proposed method extracts the useful information from the reconstructed solar irradiance ‘image’ matrix in the frequency domain to form the next second level solar irradiance. The results show that the proposed model can learn the past features comprehensively, indicating significant potential for further study.

Very Short-Term Solar Irradiance Forecasting at a Sub-Minute Scale Based on WT-Cnns