Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal alloc...
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
MDPI AG
2023-02-01
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| سلاسل: | Applied Sciences |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2076-3417/13/4/2749 |
| _version_ | 1827758733193641984 |
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| author | Elissaios Sarmas Evangelos Spiliotis Nikos Dimitropoulos Vangelis Marinakis Haris Doukas |
| author_facet | Elissaios Sarmas Evangelos Spiliotis Nikos Dimitropoulos Vangelis Marinakis Haris Doukas |
| author_sort | Elissaios Sarmas |
| collection | DOAJ |
| description | Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation of the available financial resources. This study aims to provide a machine-learningbased methodological framework for a priori predicting the energy savings of energy efficiency renovation actions. The proposed solution consists of three tree-based algorithms that exploit bagging and boosting as well as an additional ensembling level that further mitigates prediction uncertainty. The proposed models are empirically evaluated using a database of various, diverse energy efficiency renovation investments. Results indicate that the ensemble model outperforms the three individual models in terms of forecasting accuracy. Also, the generated predictions are relatively accurate for all the examined project categories, a finding that supports the robustness of the proposed approach. |
| first_indexed | 2024-03-11T09:10:40Z |
| format | Article |
| id | doaj.art-12bb22830b2046928b511025f42b51a8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:10:40Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-12bb22830b2046928b511025f42b51a82023-11-16T19:00:11ZengMDPI AGApplied Sciences2076-34172023-02-01134274910.3390/app13042749Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning ModelsElissaios Sarmas0Evangelos Spiliotis1Nikos Dimitropoulos2Vangelis Marinakis3Haris Doukas4Decision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 10682 Athens, GreeceForecasting and Strategy Unit, School of Electrical & Computer Engineering, National Technical University of Athens, 10682 Athens, GreeceDecision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 10682 Athens, GreeceDecision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 10682 Athens, GreeceDecision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 10682 Athens, GreeceEnergy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation of the available financial resources. This study aims to provide a machine-learningbased methodological framework for a priori predicting the energy savings of energy efficiency renovation actions. The proposed solution consists of three tree-based algorithms that exploit bagging and boosting as well as an additional ensembling level that further mitigates prediction uncertainty. The proposed models are empirically evaluated using a database of various, diverse energy efficiency renovation investments. Results indicate that the ensemble model outperforms the three individual models in terms of forecasting accuracy. Also, the generated predictions are relatively accurate for all the examined project categories, a finding that supports the robustness of the proposed approach.https://www.mdpi.com/2076-3417/13/4/2749energy efficiencyenergy efficiency renovationsmachine learningartificial intelligence |
| spellingShingle | Elissaios Sarmas Evangelos Spiliotis Nikos Dimitropoulos Vangelis Marinakis Haris Doukas Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models Applied Sciences energy efficiency energy efficiency renovations machine learning artificial intelligence |
| title | Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models |
| title_full | Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models |
| title_fullStr | Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models |
| title_full_unstemmed | Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models |
| title_short | Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models |
| title_sort | estimating the energy savings of energy efficiency actions with ensemble machine learning models |
| topic | energy efficiency energy efficiency renovations machine learning artificial intelligence |
| url | https://www.mdpi.com/2076-3417/13/4/2749 |
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