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Ensemble Machine Learning Approaches for Prediction of Türkiye's Energy Demand
Version 1
: Received: 6 October 2023 / Approved: 9 October 2023 / Online: 9 October 2023 (13:14:43 CEST)
A peer-reviewed article of this Preprint also exists.
Kayacı Çodur, M. Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand. Energies 2024, 17, 74. Kayacı Çodur, M. Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand. Energies 2024, 17, 74.
Abstract
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 measured their performance 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 with both 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 produced superior results compared to the other models across performance metrics. 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 our predictive model but also underscore the broader implications of this study within the framework of the United Nations' Sustainable Development Goals (SDGs).
Keywords
energy demand, ensemble machine learning, SDG’s, Türkiye,
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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