Bionic fusion perspective : Audiovisual-motivated integration network for solar irradiance prediction

Accurate and reliable prediction of solar irradiance (SI) is an important requirement to develop solar energy while a challenging task due to stochastic and nonlinear data characteristics. Additionally, most deep networks show powerful prediction capabilities but lack the supports from biological science, reflecting that bionically-inspired networks in SI analysis are still not enough explored. To this end, this paper proposes an audiovisual-motivated Transformer-CNN integration network, called ATI-net, for predicting SI. The audiovisual cognition gives a superior design framework for ATI-net with signal capture, signal analysis, and prediction blocks. In the first block, through mimicking the function of both eye and ear in external signal conversion, multi-scale features are extracted by incorporating multi-branch convolutions with varying kernels, where the Mish function addresses the problem that traditional ReLU function stops learning when the input is negative. In the second block, through mimicking the function of left and right hemispheres in neuronal signal analysis, two structures triggered by Transformers and convolutions are designed to remember temporal evolutionary rules, where residual connections are beneficial to mine deep information and avoid forgetting. In the third block, through mimicking the function of a higher brain region in generating understanding, the above information is integrated to make the SI prediction. Besides, the nonlinear dependencies and linear relationships are independently extracted and integrated into the ATI-net, which not only reduces information interference but is consistent with the “divide and conquer” idea. Experimental results show that the ATI-net outperforms 18 benchmarks, and average improvements of root mean squared error (RMSE) are 26.28% and 26.01% for two datasets, respectively. In summary, the ATI-net is one of the reliable alternatives to SI prediction.