Multiple power-based building energy management system for efficient management of building energy

Yoon, Seok-Ho; Kim, Seung‐Yeon; Park, Geon-Hee; Kim, Yi-Kang; Cho, Choong-Ho; Park, Byung-Hun

doi:10.1016/j.scs.2018.08.008

Cited by 45 publications

(18 citation statements)

References 21 publications

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“…Yoon, et al [38] Wei, et al [39] ISO 23,045 [40] Life-cycle cost (BP7) Arpke and Strong [41] Huang, et al [42] Bodily Sensation Thermal comfort (BS1)…”

Section: Influencing Aspects Measurement Indicators Referencesmentioning

confidence: 99%

Investigating the Crucial Aspects of Developing a Healthy Dormitory based on Maslow’s Hierarchy of Needs—A Case Study of Shenzhen

Liu

et al. 2020

IJERPH

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In recent years, with the development of green building and the increase of health awareness, the concept of healthy building has been proposed. Recently, studies have been made on developing healthy residential buildings; however, few attentions have been paid to the development of healthy dormitories. To bridge this research gap, this paper aims to investigate the crucial aspects of developing a healthy dormitory. Based on the Maslow’s hierarchy of needs, three influencing aspects which include 17 measurement indicators are identified. Questionnaire surveys are subsequently conducted to collect students’ perceptions on the identified indicators. After a structural equation modeling (SEM) analysis, the relationships between the three influencing aspects are analyzed. The research findings show that building performance, bodily sensation, and humanistic environment must be taken into account in the development of a healthy dormitory. In addition, it is revealed that building performance has a significant impact on bodily sensation, while bodily sensation has a significant impact on humanistic environment. However, building performance is found having little impact on humanistic environment. The findings of this study could provide useful information for the construction of healthy dormitories.

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“…Yoon, et al [38] Wei, et al [39] ISO 23,045 [40] Life-cycle cost (BP7) Arpke and Strong [41] Huang, et al [42] Bodily Sensation Thermal comfort (BS1)…”

Section: Influencing Aspects Measurement Indicators Referencesmentioning

confidence: 99%

Investigating the Crucial Aspects of Developing a Healthy Dormitory based on Maslow’s Hierarchy of Needs—A Case Study of Shenzhen

Liu

et al. 2020

IJERPH

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“…Reducing energy consumption and improving energy efficiency have become urgent concerns for urban sustainable development. Buildings, in particular, are among the world's largest energy consumers [2]. Building energy accounts for 35-40% of society's total energy consumption, which implies that there may be large energy-saving potential in buildings.…”

Section: Introductionmentioning

confidence: 99%

“…According to Ref [2], the degree-day method is the simplest energy analysis method. The degree-day method can be used for predicting building energy consumption [3], building design and regulations [4], as well as energy management [5].…”

Section: Introductionmentioning

confidence: 99%

Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Meng

Xiong

Mourshed

et al. 2020

Sustainable Cities and Society

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Mean regression analysis may not capture associations that occur primarily in the tails of the outcome distribution.In this study, we focused on building heating gas consumption related to multiple weather factors to find the extent to which they impact gas consumption at higher quantile levels. We used change-point multivariable quantile regression models to investigate distributional effects and heterogeneity in the gas consumption-related responses to weather factors. Subsequently, we analyzed quantile regression coefficients that corresponded to absolute differences in specific quantiles of gas consumption associated with a one-unit increase in weather factors. We found that the association of weather factors and gas consumption varied across 19 quantiles of gas consumption distribution. The heterogeneity of the case-study buildings was different: right tails of gas consumption for the community (CL) and educational (ED) buildings were more susceptible to weather factors than those of the health care (HL) building. The base temperature of the CL building across quantiles of gas consumption indicated a flat trend, but the uncertainty ranges were relatively large compared with those for the CL and ED buildings. This study could reveal which factors are most important and the extent to which they affect gas consumption at specific quantile levels.

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“…Residential and commercial buildings account for more than 40% of global energy consumption and produce more than one-third of the total carbon dioxide emissions [1]. In order to improve energy efficiency, it is important to provide fine-grained energy consumption information for demand-side management [2].…”

Section: Introductionmentioning

confidence: 99%

A Load Identification Method Based on Active Deep Learning and Discrete Wavelet Transform

et al. 2020

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Non-Intrusive Load Monitoring (NILM) makes it possible for users and energy providers to track the fine-grained energy consumption information of residential and commercial buildings. The load identification methods in NILM usually require labeling many samples for training and evaluation, which is always expensive and time-consuming. In order to reduce the labeling cost, this paper proposed a load identification method based on Active Deep Learning (ADL). In this method, Discrete Wavelet Transform (DWT) was applied to extract high-dimensional appliance features from original current signals. Then a pool-based or stream-based active deep learning model was built to learn the features and select high-value samples that worthy of labeling. A mixed dataset based on three public datasets was formed to evaluate the proposed method and three sampling approaches of active learning. The results showed that the proposed method could significantly reduce labeling cost on large datasets, and the number of samples required is 33% lower than the state-of-the-art method when the F1 score is equal. Compared with pool-based sampling approaches, the stream-based approach's benefits are that the classifier improved and the query frequency decreased with continuous input of samples. INDEX TERMS NILM, load identification, active deep learning, semi-supervised learning, CNN, pool-based sampling, stream-based sampling.

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Multiple power-based building energy management system for efficient management of building energy

Cited by 45 publications

References 21 publications

Investigating the Crucial Aspects of Developing a Healthy Dormitory based on Maslow’s Hierarchy of Needs—A Case Study of Shenzhen

Investigating the Crucial Aspects of Developing a Healthy Dormitory based on Maslow’s Hierarchy of Needs—A Case Study of Shenzhen

Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

A Load Identification Method Based on Active Deep Learning and Discrete Wavelet Transform

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