AdaSpline-Net for Improved Short-Term Solar Irradiance Forecasting

Neural networks utilizing backpropagation are a powerful tool for solar irradiance forecasting, which is vital for climate research and energy market operations. This study addresses the challenge of modeling complex, nonlinear relationships between weather parameters by introducing an innovative adaptive B-spline activation function. The piecewise polynomial approach proposed in this work, integrated into the neural network framework and optimized using the Adaptive Moment Estimation (Adam) algorithm, allows for effective parameter tuning after each epoch, resulting in enhanced forecasting accuracy. Unlike traditional activation functions, which suffer from issues like the "dying ReLU" problem, the adaptive B-spline function provides smooth, flexible mappings with continuous gradients, allowing it to capture intricate data patterns effectively. This adaptability makes it particularly suitable for high-precision environmental applications. Validation using real-world datasets from Singapore and Hawaii shows that the adaptive B-spline significantly outperforms conventional activation functions, delivering up to a 10% improvement in forecasting accuracy for both training and testing datasets. Furthermore, its robustness across various neural network architectures demonstrates its adaptability and compatibility with backpropagation. This research highlights the potential of optimized B-spline activation functions to improve the accuracy and dependability of neural network-based forecasting models.