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

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...

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Bibliographic Details
Main Authors: Tiago Yukio Fujii, Victor Takashi Hayashi, Reginaldo Arakaki, Wilson Vicente Ruggiero, Romeo Bulla, Fabio Hirotsugu Hayashi, Khalil Ahmad Khalil
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Machines
Subjects:
MLOps
digital twin
IoT
machine learning
prediction
Online Access:https://www.mdpi.com/2075-1702/10/1/23

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