Analysis Clustering of Electricity Usage Profile Using K-Means Algorithm

Amri, Yasirli; Fadhilah, Amanda Lailatul; Fatmawati, .; Setiani, Novi; Rani, Septia

doi:10.1088/1757-899x/105/1/012020

Cited by 25 publications

(15 citation statements)

References 5 publications

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“…For instance, house buildings with small surface and roof areas will require little amount of energy to obtain optimal heating and cooling temperature. For instance, in, 85 Tran Nguyen et al [101] Rasel et al [102] Do et al [103] Zhang et al [104] Li et al [105] Adams et al [106] Electricity Load Diagrams [67] Amri et al [107] Moslemi et al [108] Jin et al [109] --Premarathne et al [110] Hosseini et al [111] Ertugrul et al [31] Moslemi et al [108] Sen et al [113] Fenza et al [114] Bicego et al [115] Chen et al [116] SML2010 [120] Bevilacqua et al [118] Zamora-Martínez et al [117] Maggiolo et al [120] --Qin et al [121] Wollsen et al [122] Li et al [123] Liu et al [124] Combined Cycle Power Plant Dataset [126] Janoušek et al [126] Rashid et al [127] --Sun et al [128] Roni et al [129] Elfaki et al [130] Cao et al [131] 2006 and November 2010. There are some missing parameters (about 1.25%) in the dataset that are represented by the absence of values between two consecutive semi-colon attribute separators.…”

Section: Energy Efficiencymentioning

confidence: 99%

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

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Section: Energy Efficiencymentioning

confidence: 99%

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

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“…Then, the data were classified into five clusters using K-means clustering. As a result, the highest and lowest energy use in summer and spring were determined [6]. Damayanti et al used the K-harmonic clustering technique for grouping a one-year electric load profile.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters

et al. 2019

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Energy demands in the building sector account for more than 30% of the total energy use and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping pace with the growing building sector and the rising demand for energy. Analyzing the energy use pattern of buildings and planning for energy conservation in existing buildings are essential. In this research, we propose a method to analyze the energy use pattern in a building using the K-means clustering method. Initial centroids in K-means clustering are chosen randomly so that the clustering result changes every time. This instability is removed in the proposed method by the selection of initial centroids using a percentile method based on empirical cumulative distribution. The results from the proposed method have better accuracy, and the internal cohesion and separation between clusters are better than the random initialization method. Analyzing yearly electricity use using the proposed clustering method, the daily pattern of electricity use can be categorized according to the operation of buildings. For this purpose, in this research, electricity use pattern was analyzed for three to six clusters. In comparison with the university schedule, six clusters were found to be appropriate and the accuracy was 89.3%. Once daily electricity use are categorized, base electricity consumption, electricity consumption by human activities, and electricity consumption by air-conditioning can be determined. As energy consumption by usage is clarified, measures for energy consumption in university buildings can be proposed.

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“…Fussy clustering technique is found to produce more robust classes and classify the buildings according to existing similarities. Amari et al [5] used K-means clustering to analyze the electricity consumption pattern of 370 clients collected from 2011 through 2014. Using dimension reduction technique whole data were reduced to four attribute.…”

Section: Related Workmentioning

confidence: 99%

Energy conservation in university buildings by energy pattern analysis using clustering technique

Nepal¹,

Yamaha²,

Sahashi³

2019

Int. J. EQ

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The energy demand of the building sector is increasing rapidly, driven by the improved access to energy in developing countries, greater ownership and use of energy-consuming devices, and rapid growth in building floor area. Energy demands in the building sector account for more than 30% of the total energy consumption and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping up with the growing building sector and the rising demand for energy. Analyzing the energy consumption pattern of the buildings and planning for energy conservation in existing buildings are essential. In this research we proposed a method to analzse the energy pattern of university buildings using K-means clustering method. Energy consumption in Science, non-science and office buildings of university is analyzed and their respective base energy, energy consumption due to human activities and air-conditioning energy consumption is calculated. The proposed method is successful in classifying the energy consumption and will prove to be helpful in the planning of energy conservation in buildings.

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Analysis Clustering of Electricity Usage Profile Using K-Means Algorithm

Cited by 25 publications

References 5 publications

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters

Energy conservation in university buildings by energy pattern analysis using clustering technique

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