Case study results: fault detection in air-handling units in buildings

Deshmukh, Suhrid; Glicksman, Leon R.; Norford, Leslie K.

doi:10.1080/17512549.2018.1545143

Cited by 26 publications

(22 citation statements)

References 19 publications

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“…Automated FDD technologies can offer several interrelated benefits including energy savings and improved operational efficiency, utility cost savings, persistence in savings over time, streamlining operations and maintenance processes, and support for continuous energy management practices such as monitoring-based commissioning. The literature suggests that 5%-30% of commercial building energy is wasted due to problems associated with controls (Deshmukh 2018;Fernandez 2017;Granderson 2017a;Katipamula 2005;Roth 2005;Wall 2018).…”

Section: Introductionmentioning

confidence: 99%

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Lin

Kramer

Granderson

2020

Building and Environment

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Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. It showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.

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Section: Introductionmentioning

confidence: 99%

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Lin

Kramer

Granderson

2020

Building and Environment

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“…However, in-depth analysis of the error between measured and estimated was difficult because of the limited number of samples. A follow-up study with more samples and related researches [23][24][25] is required to analyze the household characteristics that have a higher error and to improve the estimation models. In addition, the heating estimation model of this study was limited to Seoul, South Korea (HDD based on 18.3 • C: 2830 • C•day).…”

Section: Discussionmentioning

confidence: 99%

Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis

et al. 2019

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The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R2 of the estimation model ranged from 0.406 to 0.703, and the maximum error between measured and estimated values was around ±30%, depending on the end use.

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“…Precise measurement of the minimum air flow is essential for energy saving through advanced VAV system control, such as DCV (demand controlled ventilation) [13], OBC (occupancy-based control) [14], and temperature setpoint reset [15]. It can also be used to fault the detection and diagnostic of air flow sensors and air flow controls in VAV systems [16][17][18]. In recent research, the virtual sensor is a method for estimating a new physical quantity using existing physical quantities [19], and has been developed and applied in many parts (water flow rate of pump [20,21], mixing air temperature [22], etc.)…”

Section: Typical Airflow Measurement Of the Vav Terminal Unitmentioning

confidence: 99%

“…Step 5: The equation for calculating the virtual air flow rate of the VAV terminal unit was derived using Equations (16)- (18).…”

Section: Verification Of Vav Virtual Air Flow Sensormentioning

confidence: 99%

“…The data of the fan speed, damper opening ratio, and damper differential pressure were collected through the BAS, and the supply air flow rate was measured using a hot-wire anemometer. Figure 3 and Equation (10) were used to apply Step 5: The equation for calculating the virtual air flow rate of the VAV terminal unit was derived using Equations (16)- (18).…”

Section: Verification Of Vav Virtual Air Flow Sensormentioning

confidence: 99%

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Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

Kim

Cho

2019

Energies

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In this study, we developed a virtual air flow sensor using an in-situ damper performance curve to secure the stability of control of the variable air volume (VAV) terminal unit, and also established the in-situ measurement procedure. The minimum air flow rate of the VAV terminal unit was related to the energy consumption, it was important to determine the minimum air flow rate suitable for the situation of each room in terms of energy saving. However, it was difficult to set the minimum air flow rate low setpoint due to the low accuracy of the air flow sensor of the VAV terminal unit. This paper suggested a virtual air flow sensing method using an in-situ damper performance curve in the VAV terminal unit. The input factors of the virtual sensor were developed on the basis of the supply fan speed and damper opening ratio, which could be easily obtained from the existing control system. In addition, the in-situ measurement procedure of the virtual air flow sensor was developed by dividing the procedure into five steps. Finally, reliability of the virtual air flow sensor was evaluated through uncertainty analysis of input variables and relative error analysis, in comparison with the conventional air flow rate measurement method. The developed virtual air flow sensor was found to have an uncertainty of up to 8.8%, and it was also found that the closer to the maximum the values of the input variables, the lower the uncertainty. In addition, verification of relative error with respect to the measured values by the hot-wire anemometer was conducted by varying operation conditions to a total of 12 cases, and as a result, relative error was found to be up to 5.6%. In addition, the results of long-term experiment showed that relative error was within about 9.5%, and thus, the feasibility for field application and control was confirmed.

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Case study results: fault detection in air-handling units in buildings

Cited by 26 publications

References 19 publications

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis

Development of Virtual Air Flow Sensor Using In-Situ Damper Performance Curve in VAV Terminal Unit

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