An Internet of Things compliant model identification methodology for smart buildings

Bekiroğlu, Korkut; Srinivasan, Seshadhri; Png, Ethan; Su, Rong; Poolla, Kameshwar; Lagoa, Constantino

doi:10.1109/cdc.2017.8264314

Cited by 5 publications

(2 citation statements)

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“…Therefore, considerable effort has been recently devoted to handling missing measurements in system identification problems while simultaneously enforcing system order [8], [9]. Most of these methods use a rank minimization (RM) method, a non-convex NP-hard problem that is mostly relaxed by using the concept of nuclear norm [9], [10]. In this scenario, the missing measurement is a decision variable of the optimization problem which compounds the computation complexity further.…”

Section: B Literature Reviewmentioning

confidence: 99%

Recursive approximation of complex behaviours with IoT-data imperfections

Bekiroğlu

Srinivasan

Png

et al. 2020

IEEE/CAA J. Autom. Sinica

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This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (ℓ 0 ) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.

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Section: B Literature Reviewmentioning

confidence: 99%

Recursive approximation of complex behaviours with IoT-data imperfections

Bekiroğlu

Srinivasan

Png

et al. 2020

IEEE/CAA J. Autom. Sinica

Self Cite

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“…Recently, the Internet of Things (IoT) as a prominent technology is transferring conventional building energy management systems (BEMS) into smart, scalable, efficient, secure, flexible, and real-time systems for easier and greater energy-savings in both residential [28][29][30][31][32] and commercial buildings [33][34][35][36][37][38]. In particular, IoT-based approaches more accurately estimate thermal and scheduling models to minimize energy used by HVAC systems [39][40][41][42][43][44]; these systems currently consume about 50% of building energy consumption in developed countries [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

2020

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Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants.Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings.iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants' EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

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