A method of probabilistic risk assessment for energy performance and cost using building energy simulation

Kensek, Karen; Noble, Douglas; Schiler, Marc

doi:10.1016/j.enbuild.2015.09.070

Cited by 33 publications

(10 citation statements)

References 22 publications

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“…The first criterion is that the parameters exhibit uncertainty or potential changes in their characteristics during operation, while the second is that such changes are expected to impact energy performance. A thorough review of the literature guided the shortlisting of the parameters shown in Table 2, which cover key technical and operational features of the buildings 9 , 38,50,51 …”

Section: Methodsmentioning

confidence: 99%

“…The choice of the number of iterations to test (ie, 10 000) was guided by the work of, Sun et al 50 who also used 10 000 runs in a study of similar nature to the current one. However, acknowledging that the ideal number of iterations is case‐specific, this study followed an incremental simulation approach starting with a batch of 1000 runs, followed by additional 4000 runs, and 5000 runs (ie, a total of 10 000 runs).…”

Section: Methodsmentioning

confidence: 99%

“…A thorough review of the literature guided the shortlisting of the parameters shown in Table 2, which cover key technical and operational features of the buildings. 9,38,50,51 . Given the focus of this study on building operation, only technical parameters that are affected by building operation are selected.…”

Section: Experimental Design and Parametric Variationmentioning

confidence: 99%

“…However, since time and computational resources were available to conduct more runs, this was done to confirm/refine the observed results. Another motivation to reach 10 000 iterations is a study by, Sun et al 50 which was very similar in nature to the current work and used 10 000 iterations in its experimental design.…”

Section: Orcid Elie Azarmentioning

confidence: 99%

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A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

Alkaabi

Cho

Mayyas

et al. 2020

Energy Science & Engineering

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Economic development, population growth, and urbanization are putting increasing pressures on critical resources, such as food, water, and electricity. 1,2 The rapid population growth and urban development are often accompanied by continual increases in CO 2 emissions, 70 percent of which are coming from cities. 3 In countries with extreme climate conditions, the demand for resources, particularly energy, is intensified. 4 For instance, the United Arab Emirates (UAE) is often ranked among the top 10 countries with the highest electricity consumption per capita (MWh/person) in the world. 5,6 When comparing different sectors of the economy, the building sector often carries the highest weight in terms of energy consumption and associated CO 2 emissions. In most developed countries, the building sector accounts for 30 to 40

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Experimental Design and Parametric Variationmentioning

confidence: 99%

Section: Orcid Elie Azarmentioning

confidence: 99%

See 2 more Smart Citations

A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

Alkaabi

Cho

Mayyas

et al. 2020

Energy Science & Engineering

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“…Its addition algorithms ⊕, subtraction algorithms Θ, and multiplication algorithms ⊗ are applied in this study [18]. The three basic algorithms were introduction as follows:…”

Section: Triangular Fuzzy Numbermentioning

confidence: 99%

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

et al. 2017

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Abstract:The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

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Dynamic thermal simulation based on building information modeling: A review

Hosseini

Shirmohammadi

Kasaeian

et al. 2021

Intl J of Energy Research

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With the emergence of the fourth industrial revolution and extensive promotion of information communication tools, it is on the construction industry to reconsider the current linear design approach and adopt more integrated design approaches that encompass dynamic thermal simulation. Building information modeling (BIM), without a doubt, is a technology, which proposes an integrated practice for building design. Design team using BIM cooperatively work on each activity loop during the design procedure and present a three-dimensional (3D) design model with all data from various disciplines attached at the final stage. The inclusion of a data-rich 3D shape into the design procedure marks the dynamic thermal simulation as a simple, accessible, and, more importantly, decisive stage. This article aims to draw the role of BIM in facilitating the inclusion of dynamic thermal simulation in building design procedures. BIM in this article is introduced as an effective remedy for dealing with the linear nature of conventional building design that is the major impediment to the inclusion of dynamic thermal simulations in the design efforts. This article goes through the conventional method's deficiencies and challenges with the BIM-based method to portray each approach's advantages and disadvantages. Renowned BIM software, interoperable simulation tools, and their distinguishing characteristics are introduced in summary to give a better insight into the application of BIM for dynamic thermal simulation purposes. Additionally, a literature survey on applying the introduced tools is

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A method of probabilistic risk assessment for energy performance and cost using building energy simulation

Cited by 33 publications

References 22 publications

A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

Dynamic thermal simulation based on building information modeling: A review

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