Proposal of the energy consumption analysis process for the residential houses using big data analytics technique

Pak, Wooguil; Kim, Inhan; Choi, Jungsik

doi:10.1093/jcde/qwab063

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Pérez-Chacón et al ( 2018) used an Apache Spark algorithm that feeds into four clustering validity indices producing prediction patterns alone, with optimization solutions to assist in the predictions produced by the algorithm. Pak et al (2021) took a different approach, consisting of a fourstage process that included data understanding, meteorological data, data processing, and analytics done with R solutions code development. The study discovered that collecting additional data including energy consumption using different approaches would improve the "R solutions" analysis model that processed the relation between energy consumption of houses and the architectural elements of the building.…”

Section: Energy Consumption In Buildingsmentioning

confidence: 99%

Analyzing Energy Consumption in Universities: A Literature Review

Alnahhal‬‏,

Antar,

Sakhrieh

et al. 2024

IJEEP

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This study investigates energy consumption in general and in universities in particular. A literature review was used as the methodology of the study to show the energy consumption purposes, major findings, research gaps, limitations, and future research about how to analyze energy consumption levels. The number of references examined was 60, with 29 references about universities and some of them are concentrating on the buildings’ consumption in the GCC area. Most of the references concentrated on investigating case studies in different countries. Analysis of the findings showed that tools such as big data analytics, machine learning, simulations, and predictive models were used. However, big data analytics was the most important one. It was found that AC systems are the most important component of energy consumption, especially in hot weather countries. The studies also showed that monitoring the consumption rates will reduce the total consumption of energy. In universities, the type of the building and the day index were found the most important factors affecting energy consumption. Factors such as number of smart devices, location, and floor space were found to have different positive and negative impacts on energy consumption from one study to another. Recommendations for future research were also presented.

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Section: Energy Consumption In Buildingsmentioning

confidence: 99%

Analyzing Energy Consumption in Universities: A Literature Review

Alnahhal‬‏,

Antar,

Sakhrieh

et al. 2024

IJEEP

View full text Add to dashboard Cite

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Data-mining-based identification of post-handover defect association rules in apartment housings

Kim

Lim

et al. 2023

Journal of Computational Design and Engineering

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With the increasing expectations of clients and the growing complexity of the built environment, property management teams are facing constant pressure to effectively manage and rectify defects for improved building operational efficiency and performance. This study aims to develop and validate a defect correlation evaluation model for project and property management professionals by specifically (i) examining the defect detection and management mechanisms of residential buildings and (ii) quantifying the mechanical characteristics of defects by using association rules mining (ARM) techniques. In addressing the limitations of current evaluation approaches, this study proposed an ARM evaluation model that integrated, contextualized, and operationalized building defects into work type, location, elements, and defect type. The association between these classifications was explored and mapped. Among the resulting 123 meaningful rules, rules occurred at a rate of about 62% of the same work type in the linked work type, nearly 193% of the same element in another element, and about 23% of the close location in the far location. In conclusion, this study informs project and property management professionals of the key and complex associations between defects of different characteristics and highlights the most common occurrence defects in residential apartment buildings. Thus, this helps reduce the ambiguity and subjectivity of prioritization in defect management and facilitates maintenance and repair planning.

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Evaluation of Urban Green Building Design Schemes to Achieve Sustainability Based on the Projection Pursuit Model Optimized by the Atomic Orbital Search

et al. 2022

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The popularization and use of green buildings are of great significance for reducing the carbon emissions of buildings and achieving sustainable development. Scientific evaluation of the green building design scheme is the key factor in ensuring the popularization and use of green buildings. To overcome the shortage of a systematic evaluation index system and comprehensive evaluation method, an evaluation index system of green building design schemes and an evaluation method based on the projection pursuit model were developed. First, according to the needs of green building development, an evaluation index system of green building design schemes was systematically constructed from the five aspects of the economy, the resource utilization index, environmental impacts, technical management, and social impacts. The calculation methods of all secondary indexes are provided in detail. Then, a novel evaluation method based on the projection pursuit model optimized by the atomic orbital search was constructed. This method searches for key influencing factors and determines the evaluation grade from the evaluation data structure, and realizes the scientific and objective evaluations of green building design schemes. Finally, the Nanchang Hengda Project was selected to conduct a detailed empirical study. The research results show that the incremental net present value of the investment, the energy consumption of the air conditioning system, and the ratio of the window area to the indoor area are the most important secondary indexes. Moreover, the environmental impact index was found to be the most important primary index. Via comparisons with different optimization algorithms and evaluation methods, the superiority of the proposed model is proven.

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Proposal of the energy consumption analysis process for the residential houses using big data analytics technique

Cited by 3 publications

References 21 publications

Analyzing Energy Consumption in Universities: A Literature Review

Analyzing Energy Consumption in Universities: A Literature Review

Data-mining-based identification of post-handover defect association rules in apartment housings

Evaluation of Urban Green Building Design Schemes to Achieve Sustainability Based on the Projection Pursuit Model Optimized by the Atomic Orbital Search

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