Deep Learning for Renewable Energy Forecasting Using N-BEATS and TCN

Abstract

As renewable energy sources such as solar and wind are increasingly integrated into modern power grids, the importance of precise load forecasting continues to grow. Reliable forecasts are essential for maintaining grid stability, optimizing resource allocation, and ensuring the effective use of renewable energy. This paper compares the performance of two advanced deep learning models-N-BEATS and Temporal Convolutional Networks (TCN)-across different forecasting horizons. The study uses real-world data that combines historical electricity demand with renewable generation and weather conditions, and applies a systematic preprocessing pipeline to enhance data quality. Performance is assessed using common error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination $\left(R^{2}\right)$. Experimental results show that N-BEATS is highly effective in short-term forecasting, where its residual and trend decomposition allows it to capture rapid fluctuations and detailed patterns. By contrast, TCN demonstrates greater strength in long-term horizons, where its dilated convolutions help capture seasonal cycles andextended dependencies. These findings suggest that the two models offer complementary advantages: N-BEATSis better suited for operational, day-to-day forecasting, while TCN is more appropriate for strategic, long-term planning in renewableintegrated power systems.