Impact of the COVID-19 on electricity consumption of open university campus buildings – The case of Twente University in the Netherlands

Xu, Sheng; Cheng, Bin; Huang, Zefeng; Liu, Tao; Li, Yuan; Jiang, Lin; Guo, Wei; Xiong, Jie

doi:10.1016/j.enbuild.2022.112723

Cited by 12 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SDUTCM and SDCA are situated on the city's outskirts, where these new suburban campuses are uniformly designed as university towns with comprehensive amenities, including supermarkets, dining options, banks, and recreational facilities, and resemble small satellite cities independent of the central urban area. Students can fulfill their daily shopping needs without commuting to the city, and commercial services are primarily concentrated [27]. The concentration of commercial services on campus does not inherently guarantee that all students' specific needs are met.…”

Section: Study Areamentioning

confidence: 99%

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Liu,

Wang

et al. 2023

Buildings

View full text Add to dashboard Cite

The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable.

show abstract

Section: Study Areamentioning

confidence: 99%

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Liu,

Wang

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…To have a more realistic overview of campus energy use, we looked into data over a 3-year period (2017 -2019) prior to the outbreak of the COVID-19 (COronaVIrus Disease 2019) pandemic (during which, the university instigated home office to comply with social distancing regulations and resulted in a rather low occupancy, which might not contribute to a normal energy use pattern of the campus, as one study conducted on 25 campus buildings in the Netherlands clearly demonstrated a significant decrease in both total and specific energy use compared to the pre-pandemic era (Xu et al, Jan., 2023)) from 18 non-residential university buildings with a total net area of 108,312 m 2 , these buildings are marked with red abbreviations in Fig. 4.…”

Section: Campus Buildingsmentioning

confidence: 99%

Performance Based Core Sustainability Metrics for University Campuses Developing Towards Climate Neutrality: A Robust Picsou Framework

Jiang¹,

Kurnitski²

2023

Preprint

View full text Add to dashboard Cite

“…These measures restricted people's activities in different types buildings, and thus affected the energy consumption pattern accordingly. Recent studies have consistently identified energy consumption increases in the residential sector [ [5] , [6] , [7] , [8] ] while decreases in the non-residential sector such as university buildings [ [9] , [10] , [11] ], municipal buildings [ 12 ], commercial buildings [ 13 ] and offices [ 14 ]. Although these studies confirmed the significant impact, it is unclear whether the impact is continuing, which is an important question to explore further for managing the energy profiles in the post-pandemic era.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Huang¹,

Gou²

2023

Journal of Building Engineering

Self Cite

View full text Add to dashboard Cite

Impact of the COVID-19 on electricity consumption of open university campus buildings – The case of Twente University in the Netherlands

Cited by 12 publications

References 46 publications

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning

Performance Based Core Sustainability Metrics for University Campuses Developing Towards Climate Neutrality: A Robust Picsou Framework

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Contact Info

Product

Resources

About