Centralized Management of HVAC Energy in Large Multi-AHU Zones

Nagarathinam, Srinarayana; Vasan, Arunchandar; P, Venkata Ramakrishna; Iyer, Shiva R.; Sarangan, Venkatesh; Sivasubramaniam, Anand

doi:10.1145/2821650.2821655

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…25 A cognitive adaptive optimization algorithm is developed to make a decision based on occupancy for controlling the thermal system. However, Nagarathinam et al 33 proposed an optimization and control model MAZIC for AHUs, which in turn was able to predict the occupancy pattern in a given zone.…”

Section: Analytical Estimation Methodsmentioning

confidence: 99%

“…102 Similarly, zonal AHUs are used to collect the occupancy traces in the given zones and to manage the HVAC system in the environment. 33 The various sensing approaches and their deliverables as discussed above are summarized in Table 1.…”

Section: Alternate Occupancy Sensing Approachesmentioning

confidence: 99%

“…• Resource optimization: The prime application of an occupancy detection system is resource optimization, for example the optimization of energy consumption in buildings, [25][26][27][28][29][30][31][32] and the control of HVAC equipment, [33][34][35][36][37][38][39][40][41][42][43] lighting systems [44][45][46] and business equipment. [47][48][49][50] • User comfort: Maintenance of indoor environment quality and temperature for occupant's comfort; for example personal thermal comfort systems, [51][52][53][54][55][56] indoor environment quality improvement 57,58 and self-operated device applications 59 that exploit the occupant's presence and location.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Occupancy detection systems for indoor environments: A survey of approaches and methods

Trivedi

Badarla

2019

Indoor and Built Environment

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Utilization of smart services for indoor environments has gained extensive interest in the past decade due to the resulting increases in energy savings, user comfort and degree of automation. Occupancy detection is a critical element in automation systems due to its potential use in controlling electrical systems and devices such as lighting, air-conditioning and ventilation. It also has a high potential for improving the performance of demand-driven applications which require fine-grained occupancy information to optimize the trade-off between energy consumption and user comfort. Occupancy detection has been researched using different estimation methods and communication technologies. However, it remains challenging to procure sensory data and to model the occupancy information accurately due to the limitations of hardware deployment and underlying cost. This paper reviews existing occupancy methods and applications, along with their underlying issues. It provides a comparative analysis of the strategies from the perspectives of cost, intrusiveness and accuracy. Additionally, a new taxonomy which classifies the occupancy sensing techniques as being conventional or as alternate sensing has been proposed.

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Section: Analytical Estimation Methodsmentioning

confidence: 99%

Section: Alternate Occupancy Sensing Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Occupancy detection systems for indoor environments: A survey of approaches and methods

Trivedi

Badarla

2019

Indoor and Built Environment

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“…temperature range for all zones is set as 24-26 • C [38], [39] (we should note that the decentralized approach of this paper can be applied to the case with various thermal comfort requirements for different zones). The outlet air temperature of the AHU is set as T c t = 15 • C (t ∈ T ).…”

Section: A Performance Evaluationmentioning

confidence: 99%

HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

Yong

Spanos

2020

IEEE Trans. Automat. Sci. Eng.

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It has been well acknowledged that buildings account for a large proportion of the worlds energy consumption. However, the energy use of buildings, especially the heating, ventilation and air-conditioning (HVAC), is far from being efficient. There still exists a dramatic potential to save energy through improving building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost while satisfying thermal comfort. In particular, the thermal couplings due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for large buildings, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of the proposed method, we first compare it with a centralized method, in which the optimal solution of a small-scale problem can be obtained. We find that this decentralized approach can almost approach the optimal solution of the problem. Further, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2]. The numeric results reveal that when the number of zones is relatively small (less than 20), the two decentralized methods can achieve a comparable performance regarding the cost of the HVAC system. However, with an increase of the number of zones in the building, the proposed decentralized approach demonstrates better performance with a considerable reduction of the total cost. Moreover, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.Note to Practitioners-Buildings accounts for a large proportion of the worlds energy consumption, especially the HVAC system. How to improve the energy efficiency of HVAC system has been recognized as an important and urgent problem for a sustainable future. Motivated by this important problem, this paper is focused on the intelligent control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost for the HVAC system while maintaining zone thermal comfort. Considering that a centralized method usually encounters computation difficulties with a large number of zones, this paper aims to develop an efficient decentralized solution. In terms of the problem, there usually exist various couplings between different zones, which both arise from the heat transfer between the neighbouring zones and the operation limits of ).the HVAC system. Moreover, this problem is nonconvex and nonlinear. Therefore, it is generally difficult to find an existing decentralized or distributed method, which are mostly established for convex optimization problems. Motivated by the recent progress on decentralized or distributed optimization for nonconvex problems, this paper proposes an efficient decentralized approach, which mainly contains three steps. In the first step, the o...

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“…These models, which can be physics-based, data-driven, or hybrid, are calibrated with or learned from the real building sensor data. Estimation techniques are typically employed to deduce inputs and physical parameters that are otherwise difficult to sense directly (e.g., internal loads, wall temperature) [130]. Model-based virtual sensors are widely used in advanced controls.…”

Section: Virtual Sensorsmentioning

confidence: 99%

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Bae

Cui

et al. 2020

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Centralized Management of HVAC Energy in Large Multi-AHU Zones

Cited by 18 publications

References 23 publications

Occupancy detection systems for indoor environments: A survey of approaches and methods

Occupancy detection systems for indoor environments: A survey of approaches and methods

HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

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