Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes
The design of a desirable, sustainable energy system needs to consider a broad range of technologies, the market landscape, and the preferences of the population. In order to elicit these preferences, both toward lifestyle factors and energy system design, stakeholder engagement is critical. One pop...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/13/4911 |
| _version_ | 1797591842443755520 |
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| author | Andrew Chapman |
| author_facet | Andrew Chapman |
| author_sort | Andrew Chapman |
| collection | DOAJ |
| description | The design of a desirable, sustainable energy system needs to consider a broad range of technologies, the market landscape, and the preferences of the population. In order to elicit these preferences, both toward lifestyle factors and energy system design, stakeholder engagement is critical. One popular method of stakeholder engagement is the deployment and subsequent analysis of a survey. However, significant time and resources are required to design, test, implement and analyze surveys. In the age of high data availability, it is likely that innovative approaches such as machine learning might be applied to datasets to elicit factors which underpin preferences toward energy systems and the energy mix. This research seeks to test this hypothesis, utilizing multiple algorithms and survey datasets to elicit common factors which are influential toward energy system preferences and energy system design factors. Our research has identified that machine learning models can predict response ranges based on preferences, knowledge levels, behaviors, and demographics toward energy system design in terms of technology deployment and important socio-economic factors. By applying these findings to future energy survey research design, it is anticipated that the burdens associated with survey design and implementation, as well as the burdens on respondents, can be significantly reduced. |
| first_indexed | 2024-03-11T01:43:07Z |
| format | Article |
| id | doaj.art-8dc570dc65654a0db5de0edcaa4ece39 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T01:43:07Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8dc570dc65654a0db5de0edcaa4ece392023-11-18T16:27:41ZengMDPI AGEnergies1996-10732023-06-011613491110.3390/en16134911Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved OutcomesAndrew Chapman0International Institute for Carbon Neutral Energy Research (WPI-I2CNER), Kyushu University, Fukuoka 819-0395, JapanThe design of a desirable, sustainable energy system needs to consider a broad range of technologies, the market landscape, and the preferences of the population. In order to elicit these preferences, both toward lifestyle factors and energy system design, stakeholder engagement is critical. One popular method of stakeholder engagement is the deployment and subsequent analysis of a survey. However, significant time and resources are required to design, test, implement and analyze surveys. In the age of high data availability, it is likely that innovative approaches such as machine learning might be applied to datasets to elicit factors which underpin preferences toward energy systems and the energy mix. This research seeks to test this hypothesis, utilizing multiple algorithms and survey datasets to elicit common factors which are influential toward energy system preferences and energy system design factors. Our research has identified that machine learning models can predict response ranges based on preferences, knowledge levels, behaviors, and demographics toward energy system design in terms of technology deployment and important socio-economic factors. By applying these findings to future energy survey research design, it is anticipated that the burdens associated with survey design and implementation, as well as the burdens on respondents, can be significantly reduced.https://www.mdpi.com/1996-1073/16/13/4911energy systemsustainabilitysystem preferencemachine learningsurvey analysis |
| spellingShingle | Andrew Chapman Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes Energies energy system sustainability system preference machine learning survey analysis |
| title | Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes |
| title_full | Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes |
| title_fullStr | Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes |
| title_full_unstemmed | Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes |
| title_short | Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes |
| title_sort | enhancing survey efficiency and predictive ability in energy system design through machine learning a workflow based approach for improved outcomes |
| topic | energy system sustainability system preference machine learning survey analysis |
| url | https://www.mdpi.com/1996-1073/16/13/4911 |
| work_keys_str_mv | AT andrewchapman enhancingsurveyefficiencyandpredictiveabilityinenergysystemdesignthroughmachinelearningaworkflowbasedapproachforimprovedoutcomes |