Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city

Sathishkumar, V E; Shin, Chang-Sun; Cho, Yongyun

doi:10.1080/09613218.2020.1809983

Cited by 60 publications

(37 citation statements)

References 37 publications

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“…Gray correlation coefficient analysis is a technique for determining whether or not variables are connected and, if so, to what degree. Major and minor variables can be estimated by multiplying typical serial arcs and the level of geometric resemblance of these curves [16,17]. Gray system theory in dealing with…”

Section: Research On Energy Consumption Of Residential Buildings Based On Gray Correlations and Cluster Analysismentioning

confidence: 99%

RETRACTED ARTICLE: Research on energy consumption of residential buildings based on gray correlation and cluster analysis

Jincan

2021

Energy Syst

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With the continuous development and the rise of green buildings in China, the greenness of buildings is being paid more and more attention, helpful to standardize and normalize the green construction process of our country. Decreasing energy consumption of residential buildings is helpful to the protection of environment and energy protection. In this way, it can accord with our country sustainable developmental strategy. This paper used the method of gray correlations and cluster analysis to do green energy consumption research on evaluation system through the analysis on various factors in the process of green construction and the current specification and requirement, and then re-used real example to analyze. From the point of the research results, our research is feasible.

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Section: Research On Energy Consumption Of Residential Buildings Based On Gray Correlations and Cluster Analysismentioning

confidence: 99%

RETRACTED ARTICLE: Research on energy consumption of residential buildings based on gray correlation and cluster analysis

Jincan

2021

Energy Syst

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“…Studies in the first category provide an in-depth description of adopting technologies for benefitting only specific tasks. These studies include acquiring highresolution geometry data of existing site conditions with 3D LiDAR scanning (Shih et al, 2019(Shih et al, , 2020, automatic generation and evaluation of a large number of design options using simulations and algorithms (Haymaker J et al, 2018;Lin & Gerber, 2014;Lorenz et al, 2020), improving design constructions with digital fabrication methods (Melenbrink et al, 2020;Wagner et al, 2020) and better understanding of built designs with data analytics (Fan et al, 2021;V E et al, 2021). They do not provide an overview of the benefits to a design project and a design practice.…”

Section: Method: Proposed Computation In Design (C-in-d) Frameworkmentioning

confidence: 99%

A framework for considering the use of computational design technologies in the built environment design process

Chen¹,

Janssen²,

Aviv³

et al. 2022

ITcon

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This research proposes a Computation in Design (C-in-D) framework for design practices to consider the adoption of computational technologies in their design process. Examples of computational design technologies include but are not limited to computational algorithms, the Internet of Things (IoT), reality capture and digital fabrications. We develop the framework by categorizing design projects based on their physical scales, defining the work stages in a design project, and decomposing the design process into tasks and data flows. The computational design technologies can then be assessed by mapping its usage onto these data flows. The framework provides a basic structure for practices to customize and systematically assess the impacts of using computational design technologies in their design process. We demonstrate the C-in-D framework in three case studies, a sculpture design, an interior retrofit, and a form-based code development. The demonstration shows that web-based interactive parametric modeling and reality capture technologies can improve collaboration between the artist and modeler in the sculpture design project. IoT and optimization algorithms can improve the daylighting performance of the interior retrofit, and the use of Geographic Information System and reality capture technology can improve site analysis and visioning of the form-based code development process. The framework is a valuable tool for facilitating the adoption of new design technologies in practice.

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“…Penelitian mengenai hal ini pernah dilakukan beberapa peneliti, diantaranya penelitian asal Korea tentang model prediksi konsumsi energi untuk smart factory menggunakan algoritma data mining oleh [3] yang memperkenalkan dan mengeksplorasi model prediksi konsumsi energi industri baja dengan menghasilkan model terbaik yaitu Random Forest dengan nilai RMSE 7,33 pada set pengujian. Selain itu, penelitian lain mengangkat judul model prediksi konsumsi energi yang efisien untuk suatu data analitik bangunan industri di kota pintar dengan menyajikan dan mengeksplorasi model konsumsi energi prediktif berdasarkan teknik penambangan data untuk industri baja skala kecil yang cerdas di Korea Selatan menggunakan variabel seperti lagging dan arus utama daya reaktif, faktor daya lagging dan arus terdepan, emisi karbon dioksida, dan jenis beban [4]. Selanjutnya penelitian asal Australia mengenai prediksi konsumsi energi industri menggunakan teknik data mining oleh [5] yang menyajikan dan mengeksplorasi model prediksi konsumsi energi menggunakan pendekatan data mining untuk industri baja hingga menunjukkan bahwa model Random Forest dapat memprediksi konsumsi energi terbaik dan mengungguli algoritma konvensional lainnya dalam perbandingan.…”

Section: Pendahuluanunclassified

Klasifikasi Konsumsi Energi Industri Baja Menggunakan Teknik Data Mining

Rahayu¹,

Purnama

2022

JTI

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Human needs in fulfilling clothing, food and housing in today's life cannot be separated from the involvement of electrical energy. In several sectors of life, namely the household sector, industry, business, social, government office buildings, and public street lighting, electricity is needed. The energy consumption industry sector is relatively higher than other sectors, so it is necessary to control energy consumption, especially in the industrial sector. As a result, for a nation or region, forecasting the use of electrical energy becomes urgent and crucial. Research on this issue has emerged from various countries, for example, research from Korea on energy consumption prediction models for smart factories using a data mining algorithm that introduces and explores the steel industry energy consumption prediction model by producing the best model, namely Random Forest with an RMSE value of 7.33 in the test set. In addition, another study raised the title of an efficient energy consumption prediction model for an analytical data of industrial buildings in a smart city by presenting and exploring a predictive energy consumption model based on data mining techniques for a smart smallscale steel industry in South Korea using variables such as lagging and current. main reactive power, lagging power factor and leading current, carbon dioxide emission and load type. Research from Australia is also not left behind, discussing the prediction of industrial energy consumption using data mining techniques which presents and explores energy consumption prediction models using a data mining approach for the steel industry to show that the Random Forest model can best predict energy consumption and outperform other conventional algorithms in comparison. This study presents a classification of energy consumption in the steel industry, in order to know the pattern of using light loads, medium loads, and maximum loads using data mining techniques on public data that is already available on this matter, with the aim that energy users in the steel industry are wiser in using energy because you already know the pattern of each load. The methods used include Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks resulting in accuracy of 91.13%, 90.50%, 70.97% and 75.56%, so that the classification method is the most suitable for use. In classifying industrial energy consumption on the steel industry energy consumption dataset, Random Forest.

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Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city

Cited by 60 publications

References 37 publications

RETRACTED ARTICLE: Research on energy consumption of residential buildings based on gray correlation and cluster analysis

RETRACTED ARTICLE: Research on energy consumption of residential buildings based on gray correlation and cluster analysis

A framework for considering the use of computational design technologies in the built environment design process

Klasifikasi Konsumsi Energi Industri Baja Menggunakan Teknik Data Mining

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