Reducing university air handling unit energy usage through controls-based energy efficiency measures

Journal of Building Performance Simulation

Newsham

Ashouri

et al. 2020

Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼ 12°C to a range of ∼ 16°C to ∼ 20°C over an outdoor temperature range of ∼ 20°C to ∼ 0°C. Energy savings are estimated as ∼ 20% relative to a reference case with a constant supply temperature and preheating/precooling period.

Section: Introductionmentioning

confidence: 99%

Deriving sequences of operation for air handling units through building performance optimization

Journal of Building Performance Simulation

Newsham

Ashouri

et al. 2020

“…In the free cooling state, when the supply air temperature is less than the return air temperature but more than the outdoor air temperature, OAF is calculated based on equation (1) (OAF= Tsa−TraToa− Tra). 32 Since OAF is an exponential function of the mixing box damper as described in equation (2) (OAF=x1,mbd⋅(Smbd)x2,mbd) , the ideal mixing box damper control signal in an economizer state will be Smbd−ideal=((Tsa−TraToa−Tra)x1,mbd)1x2,mbd .…”

Section: Methodsmentioning

confidence: 99%

A holistic sequential fault detection and diagnostics framework for multiple zone variable air volume air handling unit systems

Torabi

Building Services Engineering Research and Technology

O’Brien

et al. 2022

A holistic fault detection and diagnostics (FDD) method should explicitly consider the dependencies between faults at the system- and zone-level to isolate the root cause. A system-level fault can trigger false alarms at the zone-level, while concealing the presence of a zone-level fault. However, most FDD methods have focused on a single component/equipment without considering the importance of the interactions between zone- and system-level devices. This paper proposes a holistic hierarchical framework for FDD, combining the process of detection and diagnosis of controls hardware and sequencing logic faults affecting the actuators at the system- and zone-level. The proposed framework follows a holistic sequential procedure to diagnose faults and suppress false alarms in this order: hard faults in air handling units (AHUs), hard faults in variable air volume (VAV) zones, sequencing logic faults in AHUs, and sequencing logic faults in VAV zones. The detection of faults is performed by visualizing the discrepancies between the expected and measured operational behaviour of AHUs and VAV boxes. Examples demonstrating the framework are provided with data from 10 different VAV AHU systems. Practical application: This paper provides a sequential hierarchical FDD framework to address two main issues in VAV AHU systems: detectability and significance. Regarding detectability, the framework prioritizes hard faults over sequencing logic faults to avoid false positives and false negatives; about significance, system-level faults are prioritized over zone-level faults to triage high-impact faults in the system. The detection of faults is performed via visualizing the biases from the expected behaviour of AHU and VAV characteristics to provide an envisioning interpretation for the experts in facilities management in commercial buildings to find the root cause of the fault and fix them on-site.

“…Implementing faulty sequencing logic for HVAC operations is a common cause of inefficient building operation in terms of energy and comfort (e.g., Wang, Mathew and Pang, 52 Zhang and Bannister, 53 Gunay et al, 54 Shea, Kissock and Selvacanabady, 55 Hobson et al 20 ). Although inappropriate sequencing of operations is common, the attention given to this topic by researchers and building operation stakeholders is disproportionally low.…”

Section: The Proposed Workflowmentioning

confidence: 99%

“…Common inappropriate sequences of operation include, but are not limited to, poor supply air temperature control (e.g., static or suboptimal reset schemes), inappropriate temperature setpoints, inappropriate use of the economizer cycle, and inappropriate scheduling (e.g., hours of operations). 56 For example, Shea, Kissock, and Selvacanabady 55 investigated 12 AHUs of university buildings in Dayton, Ohio, USA, and were able to achieve 19% savings in electricity use by decreasing the supply air pressure, 35% decrease in energy use by adjusting operation schedules, and 17% decrease in cooling energy use by correcting the settings of the economizer cycle.…”

Section: The Proposed Workflowmentioning

confidence: 99%

A data-driven workflow to improve energy efficient operation of commercial buildings: A review with real-world examples

Abuimara

Hobson

Building Services Engineering Research and Technology

et al. 2022

Data-driven building operation and maintenance research such as metadata inference, fault detection and diagnosis, occupant-centric controls (OCCs), and non-invasive load monitoring have emerged (NILM) as independent domains of study. However, there are strong dependencies between these domains; for example, quality of metadata affects the usability of fault detection and diagnostics techniques. Further, faults in controls hardware and programs limit the performance of OCCs. To this end, a literature review was conducted to identify the dependencies between these domains of research. Additionally, real-world examples using operational data from three institutional buildings in Ottawa, Canada, were provided and discussed to demonstrate these dependencies. Finally, a holistic tool-agnostic workflow was introduced which suggested the implementation of operational energy efficiency measures in the following order to ensure their full potential: (1) improve metadata, (2) address faults, (3) implement OCCs, and (4) monitor enhanced key performance indicators (KPIs). The proposed workflow is intended to be comprehensive, reproducible, nonintrusive, and inexpensive to implement. Practical applications: Optimization of building operations has been emerging among energy management professionals as a relatively low-cost means to achieve energy efficiency and minimize occupants’ discomfort. To this end, this study introduces a tool-agnostic data-driven workflow to building energy management practitioners that can assist them in achieving increased energy efficiency. The proposed workflow recognizes the interdependency of the various domains of research which have historically been treated independently.