Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey

Srivastava, Charu; Yang, Zheng; Jain, Rishee K.

doi:10.1016/j.buildenv.2019.04.016

Cited by 31 publications

(9 citation statements)

References 51 publications

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“…However, data (raw numbers and symbols), which need to be converted into knowledge to take action in the building environment, cannot be used in FM because of some hindrance factors [40]. For example, Srivastava et al [25] stated that the managers, who are responsible for energy management, have a lack of energy background. To overcome the lack of background, BI&A and their simple use with user interfaces gain more importance to derive more profound knowledge and decisions from data sources [25].…”

Section: Business Intelligence and Analytics In Facility Managementmentioning

confidence: 99%

“…For example, Srivastava et al [25] stated that the managers, who are responsible for energy management, have a lack of energy background. To overcome the lack of background, BI&A and their simple use with user interfaces gain more importance to derive more profound knowledge and decisions from data sources [25].…”

Section: Business Intelligence and Analytics In Facility Managementmentioning

confidence: 99%

“…Therefore, the authors emphasized that facility managers need proper tools to manage scarce resources. However, the proposed FM systems in the literature or available commercial systems are either query-based or present limited data analytic opportunities or limited data availability depending on lack of average values (rule-based systems) or a specific area such as energy and maintenance or need for an external analysis solution such as Python, R, or Weka in terms of particular data sources (such as BIM) [15,17,[20][21][22][23][24][25][26][27]. Therefore, data-driven decisions cannot be performed during FM.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

2022

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The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities.

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Section: Business Intelligence and Analytics In Facility Managementmentioning

confidence: 99%

Section: Business Intelligence and Analytics In Facility Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

2022

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“…Owing to the difficulties in complete enumeration of low-rise dwellings, the surveys and usage information were [68][69][70], and the survey design was based on population samples. Sample sizes were measured, and the survey was developed using the following formula:…”

Section: Results Of Sensitivity Analysis Tablementioning

confidence: 99%

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

Kim¹,

Yoo

Choi

et al. 2021

Advances in Civil Engineering

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The energy consumption of low-rise residential buildings in South Korea exceeds the targets set in national policies and the standards of other countries. Moreover, there are insufficient policies in place to improve the energy performance of existing low-rise residential buildings and no means to investigate the current status. A standard model enables cost-effective and fast load forecasting and can also be used to establish long-term policies through evaluation of energy saving in buildings before and after the application of energy policies. This study developed a standard model for predicting energy consumption by reflecting the characteristics of low-rise residential buildings in Korea. The standard model was developed based on reliable related standards, national statistical data, and national reports, and the energy variables applied were validated through a sensitivity analysis. Surveys and field measurements were conducted to investigate the energy usage of 70 households in low-rise residential buildings in Korea, and the developed model was validated through comparison with the actual energy usage data. Consequently, the total energy consumption error rate was 12.67% (R2 value: 0.8164), with a significance level higher than 80%, which indicated that the developed model was highly efficient and reliable.

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“…Although many publications deliver solutions for smart building automation systems, the problem remains [2]. Usually, the complete building services and electricity systems are maintained by pure automation algorithms, i.e., on-off switch is made by facility management personnel or constant (0-24 o'clock) use of Heating, Ventilation and Air Conditioning (HVAC) systems [3]. Despite of the fact current modern office facilities are equipped with several kinds of Building Management Systems (BMSs) utilizing advanced applicable operation algorithms; these facilities are not utilized or even not programmed at the level of their full potential [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of active system operation optimization in office buildings

Bonjar

Baranyai

Horváth

et al. 2021

Pollack

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The aim of this paper is to present a solution for optimizing the operation of the Szentágothai Research Center. This building has several different functions at a high degree, so it can represent most of the difficulties in achieving an ideal level of operation energy consumption while assuring an adequate comfort level.As a first stage of a generic office building optimization research, a monitored reference building of common type was chosen. Various active operation optimization experiments were carried out. The successive reduction of operation intensity and schedules resulted in 63.3% operation energy savings. The results provide considerable potential in generic office design applications.

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Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey

Cited by 31 publications

References 51 publications

Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

Analysis of active system operation optimization in office buildings

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