Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand
Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy depe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/1/74 |
| _version_ | 1797358842393133056 |
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| author | Merve Kayacı Çodur |
| author_facet | Merve Kayacı Çodur |
| author_sort | Merve Kayacı Çodur |
| collection | DOAJ |
| description | Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs). |
| first_indexed | 2024-03-08T15:08:04Z |
| format | Article |
| id | doaj.art-09d2c80531dc4c99aa100aedb237af45 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T15:08:04Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-09d2c80531dc4c99aa100aedb237af452024-01-10T14:55:41ZengMDPI AGEnergies1996-10732023-12-011717410.3390/en17010074Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy DemandMerve Kayacı Çodur0Industrial Engineering Department, Faculty of Engineering and Architecture, Erzurum Technical University, 25200 Erzurum, TürkiyeEnergy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).https://www.mdpi.com/1996-1073/17/1/74energy demandensemble machine learningSDGsTürkiye |
| spellingShingle | Merve Kayacı Çodur Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand Energies energy demand ensemble machine learning SDGs Türkiye |
| title | Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand |
| title_full | Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand |
| title_fullStr | Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand |
| title_full_unstemmed | Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand |
| title_short | Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand |
| title_sort | ensemble machine learning approaches for prediction of turkiye s energy demand |
| topic | energy demand ensemble machine learning SDGs Türkiye |
| url | https://www.mdpi.com/1996-1073/17/1/74 |
| work_keys_str_mv | AT mervekayacıcodur ensemblemachinelearningapproachesforpredictionofturkiyesenergydemand |