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,...
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| Format: | Article |
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2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1432 |
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| author | Xuemin Huang Xiaoliang Zhuang Fangyuan Tian Zheng Niu Yujie Chen Qian Zhou Chao Yuan |
| author_facet | Xuemin Huang Xiaoliang Zhuang Fangyuan Tian Zheng Niu Yujie Chen Qian Zhou Chao Yuan |
| author_sort | Xuemin Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9fcde55a92d240d3bb9f257df24d9d42 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9fcde55a92d240d3bb9f257df24d9d422025-08-20T03:43:30ZengMDPI AGEnergies1996-10732025-03-01186143210.3390/en18061432A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature PredictionXuemin Huang0Xiaoliang Zhuang1Fangyuan Tian2Zheng Niu3Yujie Chen4Qian Zhou5Chao Yuan6China Southern Power Grid Extra-High Voltage Transmission Company, Guangzhou Bureau, Guangzhou 510663, ChinaChina Southern Power Grid Extra-High Voltage Transmission Company, Guangzhou Bureau, Guangzhou 510663, ChinaChina Southern Power Grid Extra-High Voltage Transmission Company, Guangzhou Bureau, Guangzhou 510663, ChinaChina Southern Power Grid Extra-High Voltage Transmission Company, Guangzhou Bureau, Guangzhou 510663, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaTransformers 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.https://www.mdpi.com/1996-1073/18/6/1432transformer top-oil temperaturehybrid time series forecasting modelARIMA-LSTM-XGBoostprediction accuracyfault detection |
| spellingShingle | Xuemin Huang Xiaoliang Zhuang Fangyuan Tian Zheng Niu Yujie Chen Qian Zhou Chao Yuan A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction Energies transformer top-oil temperature hybrid time series forecasting model ARIMA-LSTM-XGBoost prediction accuracy fault detection |
| title | A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction |
| title_full | A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction |
| title_fullStr | A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction |
| title_full_unstemmed | A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction |
| title_short | A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction |
| title_sort | hybrid arima lstm xgboost model with linear regression stacking for transformer oil temperature prediction |
| topic | transformer top-oil temperature hybrid time series forecasting model ARIMA-LSTM-XGBoost prediction accuracy fault detection |
| url | https://www.mdpi.com/1996-1073/18/6/1432 |
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