Lucas de Azevedo Takara

Accurate photovoltaic (PV) power forecasting remains challenging due to nonlinear dynamics, rapid weather variations, and multi-scale temporal dependencies. This paper proposes ConvChebyKAN, a hybrid deep learning architecture that integrates multi-scale causal convolutions, Chebyshev polynomial parameterized Kolmogorov Arnold networks (KANs), and conformal prediction to improve both deterministic and probabilistic forecasting performance. The multi-scale convolutional module captures short-term fluctuations and long-term temporal dependencies, while the Chebyshev-based KAN improves nonlinear representation capability and numerical stability. A multi-objective hyperparameter optimization strategy based on Pareto efficiency and TOPSIS ranking is used to jointly optimize forecasting accuracy, prediction interval sharpness, and coverage reliability. The model is evaluated using real photovoltaic generation and meteorological data from four seasonal datasets and forecasting horizons from 1 to 48 hours ahead. ConvChebyKAN reaches an MAE of 149.13 kWh, RMSE of 227.34 kWh, R² of 98.2% and sMAPE of 6.21, outperforming KAN, and other machine learning models. These results show that the proposed architecture effectively captures multi-scale temporal patterns while providing reliable uncertainty estimates, supporting PV forecasting applications in power system operation and energy management.