Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda

Smart and Sustainable Built Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Purpose Predictive digital twin technology, which amalgamates twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation predictive purposes, has demonstrated its effectiveness across a wide array industries. Nonetheless, there is conspicuous lack comprehensive research in built environment domain. This study endeavours to fill this void by exploring analysing capabilities individual technologies better understand develop successful integration use cases. Design/methodology/approach uses mixed literature review approach, involves using bibliometric techniques as well thematic critical assessments 137 relevant academic papers. Three separate lists were created Scopus database, covering AI IoT, DT, since IoT are crucial creating DT. Clear criteria applied create three lists, including limiting results only Q1 journals English publications from 2019 2023, order include most recent highest quality publications. The collected was analysed package R Studio. Findings reveal asymmetric attention various components twin’s system. There relatively greater body on representing 43 47%, respectively. In contrast, direct net-zero solutions constitutes 10%. Similarly, findings underscore necessity integrating these carbon emission prediction. Practical implications indicate that clear need more case studies investigating large-scale networks collect buildings construction sites. Furthermore, development advanced precise models imperative predicting production renewable energy sources demand housing. Originality/value paper makes significant contribution field providing strong theoretical foundation. It also serves catalyst future within For practitioners policymakers, offers reliable point reference.

Language: Английский

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Semantic digital twin creation of building systems through time series based metadata inference – A review

Energy and Buildings, Journal Year: 2024, Volume and Issue: 321, P. 114637 - 114637

Published: Aug. 6, 2024

Numerous applications are being developed to enhance the energy efficiency of building systems, including fault detection and diagnosis, performance assessment, intelligent control. For these be effectively utilised, a data connection between real virtual worlds must established. One potential solution establish this enable semantic enrichment with metadata is digital twin. Semantic twins use technologies, such as ontologies, schemas. However, creating requires substantial manual effort due need examine diverse sources information about systems normalise into schema. This review investigates whether inference based on time series from can assist in automated creation twins. To end, 53 artificial intelligence-based publications analyzed for their applicability efficiency. Three key tasks examined create twin: type classification, relation inference, extraction operational information. Based findings, future research directions proposed.

Language: Английский

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

Buildings, Journal Year: 2024, Volume and Issue: 14(6), P. 1774 - 1774

Published: June 12, 2024

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

Language: Английский

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Development and Integration of a Digital Twin Model for a Real Hydroelectric Power Plant

Sensors, Journal Year: 2024, Volume and Issue: 24(13), P. 4174 - 4174

Published: June 27, 2024

In this study, a digital twin model of hydroelectric power plant has been created. Models the entire have created and malfunction situations sensor located after inlet valve analyzed using programmable logic controller (PLC). As feature (DT), error prediction prevention function studied specifically for pressure sensor. The accuracy reliability data obtained from are compared with DT model. comparison results evaluated erroneous identified. way, it is determined whether occurring in system real or caused by measurement connection errors. case failure measurement-related malfunction, situation through twin-based control mechanism. actual failure, stopped, but errors, since calculated model, value specified region known thus there no need to stop system. This prevents production loss ensuring continuity

Language: Английский

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A review on digital twin application in photovoltaic energy systems: challenges and opportunities

JMST Advances, Journal Year: 2024, Volume and Issue: 6(3), P. 257 - 282

Published: July 20, 2024

Language: Английский

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Multi-Objective Optimization of Building Ventilation Systems Using Model Predictive Control: Integrating Air Quality, Energy Cost, and Environmental Impact

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(1), P. 451 - 451

Published: Jan. 6, 2025

This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The is showcased the simulation of four systems in 8500 m2 university building. model includes multiple objective predictive control (MPC) with three objectives: electricity cost, indoor quality CO2 emission attributed to consumption. A strategy comparison conducted between several MPC solutions different weightings rule-based strategy, which emulates current system control. novel approach evaluation proposed used this study. In comparison, “balanced” reduces energy costs by 18% compared while also providing significantly better quality. An economic achieves 24% savings some reduction, an air-quality-focused provides nearly “perfect” 11% savings. Finally, environmental shows potential prioritizing emissions over costs. way, illustrates efficient operation prioritization according stakeholder interests.

Language: Английский

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Exploring the Sustainability Benefits of Digital Twin Technology in Achieving Resilient Smart Cities During Strong Earthquake Events

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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Machine learning application in building energy consumption prediction: A comprehensive review

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112295 - 112295

Published: March 1, 2025

Language: Английский

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“Digital Twins and Energy Efficiency in Buildings: A Literature Review”

Advances in Science, Technology & Innovation/Advances in science, technology & innovation, Journal Year: 2025, Volume and Issue: unknown, P. 55 - 63

Published: Jan. 1, 2025

Language: Английский

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A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3086 - 3086

Published: March 12, 2025

The rapid development of machine learning and artificial intelligence technologies has promoted the widespread application data-driven algorithms in field building energy consumption prediction. This study comprehensively explores diversified prediction strategies for different time scales, types, forms, constructing a framework this field. With process as core, it deeply analyzes four key aspects data acquisition, feature selection, model construction, evaluation. review covers three acquisition methods, considers seven factors affecting loads, introduces efficient extraction techniques. Meanwhile, conducts an in-depth analysis mainstream models, clarifying their unique advantages applicable scenarios when dealing with complex data. By systematically combing existing research, paper evaluates advantages, disadvantages, applicability each method provides insights into future trends, offering clear research directions guidance researchers.

Language: Английский

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