A complementary fused method using GRU and XGBoost models for long-term solar energy hourly forecasting

Solar photovoltaic (PV) energy plays a vital role in global renewable energy generation. Accurate and reliable solar energy forecasting is the key to improving energy scheduling, planning, and intelligent decision-making. However, existing research mainly focuses on short-term solar energy forecasting and lacks exploration of long-term forecasting. To fill the research gap, this paper proposes a complementary fused method using GPU and XGBoost models to improve the performance of long-term solar energy hourly forecasting. The proposed method includes data preprocessing, feature engineering, training of GRU and XGBoost models, and fusion of the forecasting results. Based on historical solar energy, solar irradiance and numerical weather prediction (NWP) data (e.g. temperature, wind direction, wind speed, etc.) and features extended by feature engineering are used as input data to forecast the hourly solar energy production for the next ten days. In order to reduce the risk of overfitting individual models, a simple but effective fusion technique is used to combine the forecasting results of the XGBoost and GRU models to enhance the robustness of the model. In the 2022 Tianchi UNiLAB Algorithm Competition Track 3: Renewable Energy Power Generation Forecast, first place was won by us among 513 participating teams. To validate the scalability of the proposed method, experiments were conducted with various forecasting horizons using the competition dataset and the additional GEFCom2014 dataset. The proposed method demonstrates superior performance when compared with the state-of-the-art long-term series forecasting model DLinear, with MSE and MAE metrics relatively reduced by 28.3% and 17.4% in forecasting the future 150 steps. © 2024 Elsevier Ltd