Robust Solar Irradiance Prediction: A Hybrid Approach Using XGBoost for Feature Extraction and WaveNet for Forecasting

Abstract

Accurately forecasting solar irradiance is crucial for maximizing solar energy utilization. However, in practical applications, the complex nature of irradiance patterns and the common issue of missing data pose significant challenges, making precise predictions difficult and increasing uncertainty and instability in forecasts. This paper addresses the challenge of predicting solar power output, particularly in scenarios where equipment failures lead to inaccurate or missing data. To overcome these issues, effective preprocessing techniques are employed to improve data quality before forecasting. XGBoost is utilized for feature extraction, ensuring that the model identifies and leverages the most relevant features. Additionally, a WaveNet model is used for solar irradiance prediction, capitalizing on its computational efficiency and sensitivity to small fluctuations in the data. This integrated approach aims to enhance the accuracy of solar irradiance predictions, even in the presence of data irregularities. The results suggest that the proposed model outperforms other benchmark models in terms of performance metrics achieving an R2 score of 0.9733. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.