Adaptive Smart System for Energy-Saving Campus
Due to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To addres...
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MDPI AG
2025-04-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/92/1/36 |
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| author | Ziling Chen Ray-I Chang Quincy Wu |
| author_facet | Ziling Chen Ray-I Chang Quincy Wu |
| author_sort | Ziling Chen |
| collection | DOAJ |
| description | Due to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To address this, an adaptive smart energy-saving campus system is developed in this study to improve students’ electricity usage habits. In this system, the Internet of Things (IoT) with control interfaces is integrated to enhance convenience. Using expert system rules, the system regulates the operation of the IoT for the efficient energy-saving control of a classroom. Additionally, by incorporating a random forest classifier, the system learns users’ electricity usage habits to create a tailored energy-saving environment. Gamification is also introduced to create a reward system that stimulates users’ desire to achieve goals, thus promoting autonomous energy saving. An experiment was conducted on 62 students. In total, 59 out of 62 participants responded with a sampling error of ±2.8% at a 95% confidence level. The average system usability scale (SUS) score reached 84, surpassing the cross-industry average standard, indicating that the system is user-friendly. The average self-efficacy score for energy saving reached 4.28 (σ = 3). The system significantly impacted the participant’s motivation to enhance energy saving. The net promoter score (NPS) was 29. This indicated that, although users are generally satisfied with the system, there is still room for improvement. |
| format | Article |
| id | doaj-art-083fb70405c74c4ba29e4f77981ff99f |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-083fb70405c74c4ba29e4f77981ff99f2025-08-20T03:24:39ZengMDPI AGEngineering Proceedings2673-45912025-04-019213610.3390/engproc2025092036Adaptive Smart System for Energy-Saving CampusZiling Chen0Ray-I Chang1Quincy Wu2Department of Computer Science and Information Engineering, National Chi Nan University, Nantou 545301, TaiwanDepartment of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 106319, TaiwanDepartment of Computer Science and Information Engineering, National Chi Nan University, Nantou 545301, TaiwanDue to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To address this, an adaptive smart energy-saving campus system is developed in this study to improve students’ electricity usage habits. In this system, the Internet of Things (IoT) with control interfaces is integrated to enhance convenience. Using expert system rules, the system regulates the operation of the IoT for the efficient energy-saving control of a classroom. Additionally, by incorporating a random forest classifier, the system learns users’ electricity usage habits to create a tailored energy-saving environment. Gamification is also introduced to create a reward system that stimulates users’ desire to achieve goals, thus promoting autonomous energy saving. An experiment was conducted on 62 students. In total, 59 out of 62 participants responded with a sampling error of ±2.8% at a 95% confidence level. The average system usability scale (SUS) score reached 84, surpassing the cross-industry average standard, indicating that the system is user-friendly. The average self-efficacy score for energy saving reached 4.28 (σ = 3). The system significantly impacted the participant’s motivation to enhance energy saving. The net promoter score (NPS) was 29. This indicated that, although users are generally satisfied with the system, there is still room for improvement.https://www.mdpi.com/2673-4591/92/1/36achievement systemexpert systemgamificationinternet of thingsmachine learning |
| spellingShingle | Ziling Chen Ray-I Chang Quincy Wu Adaptive Smart System for Energy-Saving Campus Engineering Proceedings achievement system expert system gamification internet of things machine learning |
| title | Adaptive Smart System for Energy-Saving Campus |
| title_full | Adaptive Smart System for Energy-Saving Campus |
| title_fullStr | Adaptive Smart System for Energy-Saving Campus |
| title_full_unstemmed | Adaptive Smart System for Energy-Saving Campus |
| title_short | Adaptive Smart System for Energy-Saving Campus |
| title_sort | adaptive smart system for energy saving campus |
| topic | achievement system expert system gamification internet of things machine learning |
| url | https://www.mdpi.com/2673-4591/92/1/36 |
| work_keys_str_mv | AT zilingchen adaptivesmartsystemforenergysavingcampus AT rayichang adaptivesmartsystemforenergysavingcampus AT quincywu adaptivesmartsystemforenergysavingcampus |