Repair Before Predict: A Unified Cross-Domain Preprocessing Framework for Improving Renewable Energy Forecasting

This creates a significant gap between theoretical model performance and real-world forecasting reliability. Renewable energy datasets, such as those from wind, solar, and biogas systems, often suffer from incomplete timestamps, inconsistent power readings, and sensor-induced anomalies.

These issues distort temporal structure and reduce predictive accuracy, especially in sequential deep learning models. To address this challenge, we propose a unified preprocessing framework that systematically repairs, aligns, and smooths renewable energy data before model training. To systematically evaluate the impact of data quality, three experimental scenarios are considered: raw data, preprocessed data, and feature-reduced data using principal component analysis (PCA).