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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction

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Author(s):
Fujii, Tiago Yukio [1] ; Hayashi, Victor Takashi [1] ; Arakaki, Reginaldo [1] ; Ruggiero, Wilson Vicente [1] ; Bulla Jr, Romeo ; Hayashi, Fabio Hirotsugu [2] ; Khalil, Khalil Ahmad [3]
Total Authors: 7
Affiliation:
[1] Univ Sao Paulo, Polytech Sch EPUSP, BR-05508010 Sao Paulo - Brazil
[2] Fed Univ ABC UFABC, Dry Educ Labs, BR-09210580 Santo Andre, SP - Brazil
[3] Bulla Jr, Jr., Romeo, Univ Sao Paulo, Polytech Sch EPUSP, BR-05508010 Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: MACHINES; v. 10, n. 1 JAN 2022.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 20/05763-6 - Energy awareness through conversational interfaces and prediction on dynamic residential tariff context
Grantee:Victor Takashi Hayashi
Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE