TempNet: A Multi-Scale Attention-Based Deep Learning Architecture for Operational Renewable Energy Forecasting

Abstract

This paper introduces TempNet, a groundbreaking multi-scale attention-based deep learning architecture that achieves unprecedented accuracy in operational renewable energy forecasting. The proposed hybrid model integrates multi-scale convolutional neural networks with different kernel sizes of {1, 3, 5, 7) for temporal pattern extraction, bidirectional LSTM layers with 128 units each, and sophisticated attention mechanisms to address the fundamental challenges of renewable energy variability. In a comprehensive evaluation on 18 months of high-resolution renewable energy data (44,166 observations), TempNet achieved exceptional performance with near-perfect forecasting metrics (RMSE of 0.44, MAE of 0.1, R2 of 0.99), showing significant improvements compared to baseline models. The proposed architecture incorporates several critical innovations, including a robust preprocessing pipeline with IQR-based outlier detection affecting 4.2% of observations, cyclical encoding for temporal features, and advanced data augmentation strategies that increased training samples by 25%. Cross-validation analysis across temporal folds demonstrates consistent performance with a coefficient of variation below 3% for all metrics, while bootstrap validation confirms statistical significance with 95% confidence intervals. Comparing TempNet with previous methods shows its superiority. The model maintains exceptional accuracy across diverse operational scenarios, including extreme weather events where maximum absolute error remained below 2.3% of mean generation capacity, significantly outperforming the 8-12% typically reported in the literature. TempNet's computational efficiency enables real-time deployment in smart grid environments, with inference times suitable for operational forecasting horizons from 1 to 24 hours. These breakthrough results can help achieve higher renewable penetration levels, reduce reserve requirements, and improve grid operations, supporting the global transition toward sustainable energy infrastructure.