Identifying models of HVAC systems using semiparametric regression

Aswani, Anil; Master, Neal; Taneja, Jay; Smith, Virginia; Krioukov, Andrew; Culler, David; Tomlin, Claire J.

doi:10.1109/acc.2012.6315566

Cited by 55 publications

(69 citation statements)

References 15 publications

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“…We note that different representative points are selected for each fan mode because of the complex air flows generated by the fans. 4 If the reduction scheme is applied to all 723 sensing data instead of only roughly 70 in each loop, each value of λ j − λ j−1 in Step 2 is too small for the division. 5 Except for the selection of the similarity measure or entropy between data, there is little difference between the PSD-based approach in Step 1 and the mutual information-based approach presented by [22].…”

Section: Parameter Identification and Data Reductionmentioning

confidence: 99%

“…Among the many issues that have to be addressed, this study is aimed at developing a dynamical model of the temperature distribution. We are motivated in this aim by a promising HVAC control methodology, namely model predictive control (MPC), for which several models have been presented in the literature [2], [4]- [8]. However, because of insufficient numbers of sensors, the models presented in those papers had limited spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Wasa

Kasajima

Hatanaka

et al. 2018

SICE Journal of Control, Measurement, and System Integration

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: This paper investigates a heating, ventilation, and air-conditioning (HVAC) system in a data center equipped with a previously developed super-multipoint temperature sensing system. This system is expected to be a key technology for reducing the total power consumption of the HVAC system by controlling the inlet temperature distribution of the servers in real time. For this purpose, we present an overview of our fan-control system based on model predictive control. The main objective of this paper is to identify a dynamical model of temperature variations, in order to predict the future evolution of the distribution. However, the spatially high-density temperature data provided by the sensing system is not suited to the needed model accuracy, and the present modeling problem is differentiated from standard ones. We thus present a systematic scheme for the spatial density reduction of sensors by using spectral clustering and graph theory and associated techniques to acquire the dynamical model. Through simulation with real data, we finally show that the developed model achieves an accuracy of 0.58 degrees Celsius on average.

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Section: Parameter Identification and Data Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Wasa

Kasajima

Hatanaka

et al. 2018

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

show abstract

“…Aswani et al [8] propose a macrolevel model for thermal environment in a building. A semiparametric regression technique is used to derive a simple model without explicit heat load effects.…”

Section: Related Workmentioning

confidence: 99%

Thermal Modeling for a HVAC Controlled Real-Life Auditorium

Sha

et al. 2014

2014 IEEE 34th International Conference on Distributed Computing Systems

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Abstract-The largest source of energy consumption in buildings is heating, ventilation, and air conditioning (HVAC). For an HVAC system to provide comfort and minimize energy consumption, it is crucial to understand the spatio-temporal thermal dynamics, especially in large open spaces. To optimize HVAC control, it is important to establish accurate dynamic thermal models. For this purpose, we constructed a real-world testbed by instrumenting an HVAC-controlled auditorium using multiple types of sensors. Based on the dataset, we develop and evaluate a novel data-driven approach to model the complex thermal dynamics in a large space through a combination of data clustering and system identification techniques. Real-world data shows that our approach achieves low estimation errors. Our modeling approach therefore provides a practical foundation for HVAC control and optimization for large open spaces.

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“…However, recent research is more focused on either increasing the efficiency of equipment or increasing the efficiency via control [26]. Very little work has been done in the area of situational awareness for building managers, and identifying sub-optimal behavior in existing BEMS [16], [27], [28].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Wijayasekara

Manic

Rieger

2013

2013 6th International Symposium on Resilient Control Systems (ISRCS)

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Abstract-Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount of information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.

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Identifying models of HVAC systems using semiparametric regression

Cited by 55 publications

References 15 publications

Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Thermal Modeling for a HVAC Controlled Real-Life Auditorium

Fuzzy linguistic knowledge based behavior extraction for building energy management systems

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