A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction

Transformers are essential for voltage regulation and power distribution in electrical systems, and monitoring their top-oil temperature is crucial for detecting potential faults. High oil temperatures are directly linked to insulation degradation, a primary cause of transformer failures. Therefore, accurate oil temperature prediction is important for proactive maintenance and preventing failures. This paper proposes a hybrid time series forecasting model combining ARIMA, LSTM, and XGBoost to predict transformer oil temperature. ARIMA captures linear components of the data, while LSTM models complex nonlinear dependencies. XGBoost is used to predict the overall oil temperature by learning from the complete dataset, effectively handling complex patterns. The predictions of these three models are combined through a linear-regression stacking approach, improving accuracy and simplifying the model structure. This hybrid method outperforms traditional models, offering superior performance in predicting transformer oil temperature, which enhances fault detection and transformer reliability. Experimental results demonstrate the hybrid model’s superiority: In 5000-data-point prediction, it achieves an MSE = 0.9908 and MAPE = 1.9824%, outperforming standalone XGBoost (MSE = 3.2001) by 69.03% in error reduction and ARIMA-LSTM (MSE = 1.1268) by 12.08%, while surpassing naïve methods 1–2 (MSE = 1.7370–1.6716) by 42.94–40.74%. For 500-data-point scenarios, the hybrid model (MSE = 1.9174) maintains 22.40–35.53% lower errors than XGBoost (2.4710) and ARIMA-LSTM (3.6481) and outperforms naïve methods 1–2 (2.8611–2.9741) by 32.97–35.53%. These results validate the approach’s effectiveness across data scales. The proposed method contributes to more effective predictive maintenance and improved safety, ensuring the long-term performance of transformer equipment.