A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting

The randomness and volatility of solar irradiance pose a challenge to efficient solar energy development and utilization across the world, which increases the necessity of developing an efficient solar irradiance forecasting model. However, previous studies rarely emphasized the importance of transfer learning and nonlinear feature selection, especially for the studies associated with newly-built photovoltaic plants. Moreover, multi-step ahead forecasting research is limited, despite its significance to dispatching efficiency improvement of photovoltaic system. Aiming to address the research gaps, a novel hybrid deep learning framework (HDLF), consisting of the modules of feature selection, feature convolution, forecasting, self-attention, transfer learning, performance evaluation, and performance analysis, is newly proposed in this paper to perform multi-step ahead solar irradiation forecasting. Specifically, the HDLF is pre-trained based on the studied datasets of global horizontal irradiation in the source domains, and the abstract information obtained from pre-training is transferred to the HDLF of the target domain to enhance its performance. To validate the effectiveness of the proposed HDLF, two simulation experiments are carried out based on the datasets from California, USA, with a resolution of 1 h. The corresponding experimental results from the modules of performance evaluation and performance analysis indicate that the maximum improvements in mean absolute error reach 80.19% and 35.67% in two experiments, respectively, thereby confirming the superiority and feasibility of the HDLF.