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

Fujii, Tatiane Mieko de Meneses; Hayashi, Victor Takashi; Arakaki, Reginaldo; Ruggiero, Wilson Vicente; Bulla, Romeo; Hayashi, Fabio Hirotsugu; Khalil, Khalil Ahmad

doi:10.3390/machines10010023

Cited by 20 publications

(7 citation statements)

References 46 publications

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“…Some studies have concentrated on the MLOps paradigm from a theoretical point of view [46], while others have presented various tools which could be applied in an MLOps platform [40,47]. Additionally, several studies have implemented portions of the overall solution [48,49]. However, there is a noticeable gap in the MLOps literature, providing a solution that integrates all these components, offering a first approach for a functional and tested MLOps architecture.…”

Section: Open-source Architectures and Mlopsmentioning

confidence: 99%

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

Cob-Parro,

Lalangui,

Lazcano

2024

Agronomy

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As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this “land gap” and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle.

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Section: Open-source Architectures and Mlopsmentioning

confidence: 99%

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

Cob-Parro,

Lalangui,

Lazcano

2024

Agronomy

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“…Lastly, utilizing these data effectively necessitates advanced analytic tools and algorithms, along with expertise to interpret and apply these data, making the choice, training, and deployment of machine learning models crucial. For instance, employing deep learning networks to predict a building's energy consumption [98,99] and operational states requires ensuring the accuracy, robustness, and real-time response of algorithms. Also, the design of machine learning-based algorithms should incorporate ethical and legal perspectives, which is necessary to achieve a trustworthy way to minimize human uncertainty.…”

Section: Complexity In Data Collection and Fusionmentioning

confidence: 99%

A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry

Zhang,

Wei,

Court

et al. 2024

Buildings

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Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.

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“…As demonstrated in this case study, energy consumption can be predicted in real time by DT measures, providing insights into necessary measures to be taken and thereby facilitating more informed decisions for energy management. The realtime functionality is also crucial for ensuring efficient energy use that adapts to dynamic environmental conditions [67,68]. It will benefit management if the model can be calibrated with real-time data to improve accuracy.…”

Section: Implications Of Energy Efficiency and Managementmentioning

confidence: 99%

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

Tahmasebinia,

Lin,

et al. 2024

Buildings

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The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

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

Cited by 20 publications

References 46 publications

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm

A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

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