Mining building performance data for energy-efficient operation

Ahmed, Ammar; Korres, Nicholas E.; Ploennigs, Joern; Elhadi, Haithum; Menzel, Karsten

doi:10.1016/j.aei.2010.10.002

Cited by 84 publications

(58 citation statements)

References 40 publications

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“…IFC 도입 초기에는 BIM 상용 저작도구의 상호 호환성을 테스 트하는 연구가 활발히 진행되었으며 (Froese et al, 1999;Kiviniemi, 2009;Lipman et al, 2011), 국내에서도 유사한 연구가 수행되 었다 (Kang et al, 2008;Lee et al, 2009;Kim and Ock, 2009;Kim and Choi., 2010) 통해 추려낸다 Ahmed et al, 2011). (Fig.…”

Section: )Bim 저작도구 간 호환성 검증unclassified

Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models

Koo¹,

Shin²

2017

Journal of KIBIM

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Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

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Section: )Bim 저작도구 간 호환성 검증unclassified

Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models

Koo¹,

Shin²

2017

Journal of KIBIM

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“…If patterns in energy consumption are not obvious, DM has been shown to be the most effective mean of capturing consumption trends in the case of efficiently maintaining buildings [7]. Also, in a study which applied DM techniques to optimise building heating, ventilation and air-conditioning performance, DM has been shown to accurately model the operation of buildings, when supplied with sufficient data [8].…”

Section: Data Mining In Energy Engineering Applicationsmentioning

confidence: 99%

Utilising the Cross Industry Standard Process for Data Mining to Reduce Uncertainty in the Measurement and Verification of Energy Savings

Gallagher

Bruton

O’Sullivan

2016

Data Mining and Big Data

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Abstract. This paper investigates the application of Data Mining (DM) to predict baseline energy consumption for the improvement of energy savings estimation accuracy in Measurement and Verification (M&V). M&V is a requirement of a certified energy management system (EnMS). A critical stage of the M&V process is the normalisation of data post Energy Conservation Measure (ECM) to pre-ECM conditions. Traditional M&V approaches utilise simplistic modelling techniques, which dilute the power of the available data. DM enables the true power of the available energy data to be harnessed with complex modelling techniques. The methodology proposed incorporates DM into the M&V process to improve prediction accuracy. The application of multi-variate regression and artificial neural networks to predict compressed air energy consumption in a manufacturing facility is presented. Predictions made using DM were consistently more accurate than those found using traditional approaches when the training period was greater than two months.

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“…However, clustering is not the only technique that has been employed. Ahmed et al [37] used classification models to estimate indicators of building behavior, such as comfort and room usage. They concluded that their approach produced better results than traditional analytical tools.…”

Section: Analysis Of Infrastructures and Retrofittingmentioning

confidence: 99%

“…The authors used those rules to achieve near-optimal supervisory control strategies for a mixed-mode building during the cooling season. Although a variety of techniques can be applied to extract IF-THEN rules, most authors use classification techniques [32,33], especially decision tree algorithms [31,28]. In addition, classification models are effective tools that can be used to predict building user comfort under different environmental conditions [28].…”

Section: Building Operationmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

221

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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Mining building performance data for energy-efficient operation

Cited by 84 publications

References 40 publications

Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models

Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models

Utilising the Cross Industry Standard Process for Data Mining to Reduce Uncertainty in the Measurement and Verification of Energy Savings

Data science for building energy management: A review

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