A case study: The energy performance gap of the Center for Interactive Research on Sustainability at the University of British Columbia

Salehi, M T Zahraei; Cavka, Belgin Terim; Frisque, Andrea; Whitehead, Derek; Bushe, W. Kendal

doi:10.1016/j.jobe.2015.09.002

Cited by 35 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a gap between experimental results and actual results. Energy consumption as predicted analytically differs widely from actually measured energy consumption [15,17,51]. The reason is user acceptability of the schedules made.…”

Section: Problem Statement and Contributionmentioning

confidence: 91%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

View full text Add to dashboard Cite

Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at "low-priced" hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,"User Comfort Level (UCL)" is formulated on the basis of user preferences on cost saving, tolerance in delay regarding use of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

show abstract

Section: Problem Statement and Contributionmentioning

confidence: 91%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Lastly, EPGs are caused by a delay in verification techniques and performance measurements for each specific technology once the building is constructed. [20] Domestic Gas, Electricity Monitoring Menezes et al [21] Non-Domestic Ventilation Post-Occupancy Evaluation (POE) Olivia et al [22] Hospital, School Indoor Comfort POE, Monitoring Choi et al [23] Non-Domestic Ventilation POE Hossein et al [24] Non-Domestic Electricity POE Salehi et al [25] Non-Domestic Ventilation Dynamic Simulation Niu et al [26] Domestic Ventilation POE Herrando et al [27] Non-Domestic Ventilation Dynamic Simulation Min et al [28] Non-Domestic Air Handling Unit Facility Management Review…”

Section: Engineering Analysis Methods and Its Thresholdmentioning

confidence: 99%

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

View full text Add to dashboard Cite

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

show abstract

“…Additionally, some studies measured the wind speed [15,27,45], wind direction [27,45], solar radiation [15,27,31,37,45,56], precipitation [15], relative humidity [15,45], as well as VOC's [57]. A few studies did not explicitly mention which variables were measured but maintained that there was a weather station on site [12,36,50,52,92].…”

Section: Empirical Datamentioning

confidence: 99%

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

et al. 2021

View full text Add to dashboard Cite

Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.

show abstract

A case study: The energy performance gap of the Center for Interactive Research on Sustainability at the University of British Columbia

Cited by 35 publications

References 19 publications

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

Contact Info

Product

Resources

About