A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting

Abstract

Deep learning models are increasingly being used to predict renewable energy-related variables, such as solar radiation and outdoor temperature. However, the black-box nature of these models results in a lack of interpretability in their predictions, and the design of deep network architectures significantly impacts the final prediction outcomes. The introduction of Kolmogorov–Arnold Network (KAN) provides an excellent solution to both of these issues. We hope that the KAN mechanism can provide fully interpretable neural network models, enhancing the potential for practical deployment. At the same time, KAN is capable of achieving good prediction results across various network architectures and neuron counts. We conducted case studies using real-world data from the Tokyo Meteorological Observatory to predict solar radiation and outdoor temperature, comparing the results with those of commonly used recurrent neural network baseline models. The results indicate that KAN can maintain model performance regardless of the chosen number of neurons. For instance, in the solar radiation prediction task, the KAN with a single hidden neuron reduces the MSE error by 75.33% compared to the baseline model. More importantly, KAN allows for the quantification of each step in the network’s computations, thereby enhancing overall interpretability.