Restaurant Energy Use Benchmarking Guideline

Hedrick, Roger; Smith, Viv; Field, K.

doi:10.2172/1019165

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Besides, the placement of ovens and the fridges, which was not ideal and poor ventilation in the kitchens may also increase the workload for the fridges to stay cool. Other factors that could influence the energy usage per unit of floor area are quantity of meals served, operation hours, types of foodservice, menu items number and complexity, and productivity standards, which vary extremely among foodservice facilities (39). Several strategies that can be done to conserve electricity in the cafés are by having an efficient kitchen layout, using energy-efficient equipment and lighting, reducing the kitchen ambient temperature, performing equipment maintenance and practising good kitchen protocols (40).…”

Section: Carbon Footprint From Sungai Petani Cafés Was Comparable To ...mentioning

confidence: 99%

The Covid-19 Pandemic Outcomes on Food Waste and Carbon Footprint of Cafés in Bandar Baru Bangi and Sungai Petani, Malaysia

Mohd Iszairi,

Zulkifli,

Zulkifli

et al. 2023

JUMMEC

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Cafés, a subcategory of the foodservice industry, are popular as a place to eat and meet with others, among Malaysians and the world population. Their operation partly contributes to food waste and carbon footprint. However, the volume of these parameters fluctuated throughout the COVID-19 pandemic. Further, there is a need to diversify the food waste data of foodservice establishments in Malaysia, across different regions and cultures. Therefore, we conducted an environmental assessment of ten cafés in two of Malaysia’s cafe hot spots, Sungai Petani and Bandar Baru Bangi, to determine their food waste (preparation loss and plate loss) and carbon footprint generation in a week when the COVID-19 restrictions were relaxed. Data on carbon footprint were collected by reading the electricity and water meters and calculated using emission factors. Sungai Petani generated substantially more food waste (77.68 % in total, equivalent to 93.04 kg) than Bandar Baru Bangi (22.32 %, equivalent to 26.73 kg), as revealed by material flow and descriptive analyses. Similarly, the carbon footprint from water consumption in Sungai Petani was 1.03 kgCO2e, generated from 3m3 water consumption, whereas Bandar Baru Bangi produced 0.82 kgCO2e of carbon footprint from 2.39 m3 water consumed, both districts on a Sunday. Contrarily, the carbon footprint related to the electricity usage in both locations was comparable. The largest amount of carbon footprint produced in Sungai Petani was on Sunday, while Saturday showed the highest carbon footprint in Bandar Baru Bangi. These findings will help the future redirection of Malaysia cafés’ management towards more sustainable practices, which can be enhanced with the help of information on customer footfall.

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Section: Carbon Footprint From Sungai Petani Cafés Was Comparable To ...mentioning

confidence: 99%

The Covid-19 Pandemic Outcomes on Food Waste and Carbon Footprint of Cafés in Bandar Baru Bangi and Sungai Petani, Malaysia

Mohd Iszairi,

Zulkifli,

Zulkifli

et al. 2023

JUMMEC

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“…Benchmark Metrics. Literature [1,2] reveals that the benchmark metrics most commonly employed in IKs are ratios between energy consumption and IK characteristics like size, seats, and meals served. However, additional metrics are needed, in particular metrics that provide estimates of the level of quality of the IK equipment, for example by comparing the ideal operation, operation in the past and current operation of appliances it would be possible to provide the IK operators with instant snapshots of the kitchen operation.…”

Section: Waste Monitoringmentioning

confidence: 99%

“…Industrial Kitchens are among the highest consumers of electricity and water [1,2], as well as of waste production. Still, very little attention has been devoted to this area by the research community.…”

Section: Introductionmentioning

confidence: 99%

Future Industrial Kitchen

Pereira

Aguiar²,

Vasconcelos³

2019

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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Large amounts of electricity, water, and food are used every day in Industrial Kitchens (IK). Still, very little attention has been devoted by the research community to this potential source of resource over-consumption. This abstract paper builds on the deployment of sensing technology in three IKs to present the main challenges and potential research directions towards more sustainable IKs. CCS CONCEPTS • Social and professional topics → Sustainability; • Computing methodologies → Machine learning approaches.

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“…A customized portfolio benchmarking method for restaurants was explored by Hedrick et al [10]. The authors demonstrated that a benchmarking system tailored to each restaurant is the best way to evaluate performance.…”

Section: Review Of Energy Performance Benchmarking Practicesmentioning

confidence: 99%

A model-based decision support tool for building portfolios under uncertainty

Boxer

Henze

Hirsch

2017

Automation in Construction

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Despite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.

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Restaurant Energy Use Benchmarking Guideline

Cited by 5 publications

References 4 publications

The Covid-19 Pandemic Outcomes on Food Waste and Carbon Footprint of Cafés in Bandar Baru Bangi and Sungai Petani, Malaysia

The Covid-19 Pandemic Outcomes on Food Waste and Carbon Footprint of Cafés in Bandar Baru Bangi and Sungai Petani, Malaysia

Future Industrial Kitchen

A model-based decision support tool for building portfolios under uncertainty

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