Automatic assisted calibration tool for coupling building automation system trend data with commissioning

Zibin, Nicholas; Zmeureanu, Radu; Love, James A.

doi:10.1016/j.autcon.2015.10.004

Cited by 19 publications

(13 citation statements)

References 18 publications

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“…Calibration can significantly improve the validity of and confidence in energy models while being used to: (1) compare the cost-effectiveness of ECMs in the design stage, (2) assess various performance optimization measures during the operational stage (Coakley et al 2014), (3) implement continuous commissioning or fault detection measures to identify equipment malfunction (Reddy 2006;Zibin et al 2016), and (4) support decision making in existing building retrofits, assessing the benefits and uncertainties associated with each (Reddy 2006;Heo et al 2012).…”

Section: Model Calibrationmentioning

confidence: 99%

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Building simulation: Ten challenges

2018

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Section: Model Calibrationmentioning

confidence: 99%

“… An automatic assisted calibration tool was developed that couples building automation system trend data with building commissioning tasks. This tool reduces the considerable time required to manually process and analyze large sets of trend data for use in calibrated simulation (Zibin et al 2016).…”

Section: Model Calibrationmentioning

confidence: 99%

Building simulation: Ten challenges

2018

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“…BEM calibration starts with an initial building energy model, weather file, and measured building performance, e.g., annual or monthly electricity and natural gas usage. Calibration can significantly improve the accuracy and reliability of energy models that are used in processes like retrofit design optimization, commissioning (Zibin, Zmeureanu, and Love 2016) and fault detection analysis (Reddy 2006).…”

Section: Introductionmentioning

confidence: 99%

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

et al. 2022

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Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

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“…-Peter Drucker Building energy consumption is poorly understood, with many not achieving their expected energy performance (Fedoruk, et al, 2015;Mallory-Hill & Gorgolewski, 2018); discrepancies between simulated (design-phase models) and measured energy use can range as high as 70-80% (De Wilde, 2014;Menezes, et al, 2012). The use of IoT devices has significantly improved this precision, for example (Kim, et al, 2015;Zibin, et al, 2016). The value of building monitoring is well-established to maintain efficient building operation.…”

Section: Introductionmentioning

confidence: 99%

BAS Data Streaming for Smart Building Analytics

Misic¹,

Gilani²,

McArthur³

2020

CIB W78 Proceedings

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Many existing and new buildings are equipped with building automation system (BAS). BAS-integrated sensors continuously monitor environmental conditions, energy use, HVAC and lighting systems, and occupancy in buildings, collecting vast amounts of data that can be of great value for building performance optimization from both the energy use and occupant comfort perspectives. However, the heterogeneity and volume of these data pose significant barriers to their use. For the effective analysis of the collected BAS data to facilitate actionable use of them and support smart buildings, advanced analytics methods such as artificial intelligence should be deployed in real time or near real time, requiring a coherent data management strategy (streaming, pre-processing, and structuring) and integration with advanced analytics techniques. A case study whereby BAS data are collected in an academic building in Toronto, Canada, is streamed to a cloud-hosted research platform Using the BACnet software, data acquired by various sensors are collected by a BAS and streamed as tuples through a Virtual Private Network (VPN) to the cloud using Transmission Control Protocol/Internet Protocol (TCP/IP) packet messages to ensure information security. The destination of the information was an ElasticSearch (ES) cluster, which is also used as a search and analytics engine on the back end. The data streaming, pre-processing, and structuring into an ontology to support facility management and complex event processing is described in this paper along with insight regarding the stakeholder planned uses and expected benefits.

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Automatic assisted calibration tool for coupling building automation system trend data with commissioning

Cited by 19 publications

References 18 publications

Building simulation: Ten challenges

Building simulation: Ten challenges

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

BAS Data Streaming for Smart Building Analytics

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