A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction
Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/1/23 |
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| author | Tiago Yukio Fujii Victor Takashi Hayashi Reginaldo Arakaki Wilson Vicente Ruggiero Romeo Bulla Fabio Hirotsugu Hayashi Khalil Ahmad Khalil |
| author_facet | Tiago Yukio Fujii Victor Takashi Hayashi Reginaldo Arakaki Wilson Vicente Ruggiero Romeo Bulla Fabio Hirotsugu Hayashi Khalil Ahmad Khalil |
| author_sort | Tiago Yukio Fujii |
| collection | DOAJ |
| description | Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction. |
| first_indexed | 2024-03-10T01:06:36Z |
| format | Article |
| id | doaj.art-936adef558274729b03382c6c3f0ebcf |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T01:06:36Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-936adef558274729b03382c6c3f0ebcf2023-11-23T14:26:13ZengMDPI AGMachines2075-17022021-12-011012310.3390/machines10010023A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption PredictionTiago Yukio Fujii0Victor Takashi Hayashi1Reginaldo Arakaki2Wilson Vicente Ruggiero3Romeo Bulla4Fabio Hirotsugu Hayashi5Khalil Ahmad Khalil6Polytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilPolytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilPolytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilPolytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilPolytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilDry Educational Laboratories, Federal University of ABC (UFABC), Santo André 09210-580, BrazilPolytechnic School (EPUSP), University of São Paulo, São Paulo 05508-010, BrazilUsing extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction.https://www.mdpi.com/2075-1702/10/1/23MLOpsdigital twinIoTmachine learningprediction |
| spellingShingle | Tiago Yukio Fujii Victor Takashi Hayashi Reginaldo Arakaki Wilson Vicente Ruggiero Romeo Bulla Fabio Hirotsugu Hayashi Khalil Ahmad Khalil A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction Machines MLOps digital twin IoT machine learning prediction |
| title | A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction |
| title_full | A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction |
| title_fullStr | A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction |
| title_full_unstemmed | A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction |
| title_short | A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction |
| title_sort | digital twin architecture model applied with mlops techniques to improve short term energy consumption prediction |
| topic | MLOps digital twin IoT machine learning prediction |
| url | https://www.mdpi.com/2075-1702/10/1/23 |
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