Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling Study

Основные понятия

The study integrates edge computing, digital twins, and deep learning to optimize building operations by enhancing indoor climate understanding. The approach focuses on deploying parametric digital twins and deep learning models on the edge for low latency and privacy compliance.

Аннотация

The study explores an integrated solution using edge computing, digital twins, and deep learning to enhance building indoor climate modeling. It emphasizes the deployment of parametric digital twins and deep learning models on the edge for efficient performance. The research includes a case study in a historic building in Sweden to evaluate different deep learning architectures' performance in predicting indoor temperature and relative humidity. The findings highlight the effectiveness of the time-series dense encoder model in multi-horizon forecasting with low computational costs.

Статистика

37 zettabytes of data were collected globally in 2020.
A total of 13 sensor boxes were distributed throughout the main building from basement to attic.
LSTM model had an inference time ranging from 308 to 330 milliseconds for temperature predictions across different rooms.
TiDE model outperformed other models with an inference time ranging from 274 to 308 milliseconds for temperature predictions across different rooms.

Цитаты

"Both parametric digital twin and deep learning-based predictive models are deployed on edge for low latency and privacy compliance."
"A suitable indoor climate is crucial for human comfort even in modern HVAC-equipped buildings."
"The TiDE model demonstrated superior performance with low computational costs."

Ключевые выводы из

Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling

by Zhon... в arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04326.pdf

Дополнительные вопросы

How can integrating occupancy-related features improve indoor climate prediction accuracy?

Integrating occupancy-related features in indoor climate prediction models can significantly enhance accuracy by capturing the impact of human behavior on the environment. Occupancy data, such as the number of people present in a room or building at different times, can influence factors like temperature and humidity due to body heat, exhalation moisture, and activity levels. By incorporating these features into predictive models, fluctuations caused by occupants can be better understood and accounted for in forecasting. This leads to more precise predictions that align with real-world conditions, especially during periods of high or low occupancy.

What are the potential challenges of deploying edge-based digital twins in various built environments?

Deploying edge-based digital twins in diverse built environments may pose several challenges:

Data Processing Limitations: Edge devices have limited computational power compared to cloud servers, which could restrict the complexity and size of digital twin models that can be deployed.
Connectivity Issues: Ensuring stable network connectivity at all deployment locations is crucial for real-time data processing and synchronization between edge devices.
Security Concerns: Edge devices are more susceptible to physical tampering or unauthorized access compared to centralized cloud servers, raising security risks for sensitive building data stored within digital twins.
Scalability: Managing a large number of distributed edge devices across multiple buildings or sites requires robust infrastructure and monitoring systems to ensure seamless operation.

How can this integrated solution be adapted beyond buildings to other industries or applications?

This integrated solution leveraging edge computing, digital twins, and deep learning techniques has broad applicability beyond buildings:

Manufacturing Processes: Implementing parametric digital twins combined with predictive maintenance algorithms can optimize equipment operations in manufacturing plants by predicting failures before they occur.
Transportation Systems: Applying similar methodologies could enhance traffic management systems through real-time analysis of vehicle movement patterns using edge computing resources.
Healthcare Facilities: Utilizing digital twins coupled with AI-driven analytics could improve patient care by predicting equipment maintenance needs or optimizing resource allocation based on historical data trends.

By customizing the parameters within this integrated framework according to specific industry requirements, it becomes versatile enough to address a wide range of use cases beyond traditional building applications while maintaining its core benefits related to efficiency optimization and proactive decision-making capabilities.