A Study on Classifying Building Energy Consumption Pattern Using Actual Building Energy Data

Woo, Hei-Jee; Choi, Ki-won; Kim, Hyeon Soo; Auh, Jin Sun; Cho, Soo Youn; Baek, Jumi; Kim, Gi-Seok; Leigh, Seung-Bok

doi:10.5659/jaik_pd.2016.32.5.143

Cited by 4 publications

(3 citation statements)

References 7 publications

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Section: Clustering Resultsmentioning

confidence: 99%

“…A comparison was made to determine which of the two clustering algorithms correctly analyzes building energy data characteristics and energy consumption patterns. The four qualitative evaluation items are as follows [7,52]: the shape of a cluster and density of color, slope of an inverse model, sensitivity of an outlier (determined by the mean of the absolute values of the standardized variables), and grades that are accurately distributed against the amount of energy consumption when it is graded based on relative criteria. Table 4 was developed to determine which of the two clustering algorithms can accurately analyze the data features and energy consumption patterns of the building by weighting the above-mentioned four qualitative evaluation indices.…”

Section: Clustering Resultsmentioning

confidence: 99%

“…Most studies that have analyzed the energy performance of buildings have focused on an analysis or estimation of the energy consumption by each area of a building, on the quantitative prediction of energy savings, and on verification of the efficiency of heating and cooling systems. Rather than relying on actual, measured data, these studies have provided estimates by modeling the physical characteristics of buildings (e.g., building surfaces and volumes), the number and behavior of occupants, and the functioning of the heating/cooling systems [7].…”

Section: Introductionmentioning

confidence: 99%

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Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

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Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

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Section: Clustering Resultsmentioning

confidence: 99%

Section: Clustering Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

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

Pak

Kim

Choi

2021

Journal of Computational Design and Engineering

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Recently, nations around the world have been implementing various policies to reduce energy consumption by improving “building energy performance” at the governmental level. In addition, “the public data opening system” has been institutionalized so that private companies could reproduce useful information by utilizing public data. However, it is insufficient to improve the energy performance of residential houses by analysing the actual energy consumption of residential houses using public open data. This study proposes a “Big Data Analysis Process for Residential Housing Energy Consumption” by utilizing public open data. This process is organized into four stages as follows: Data Understanding, regarding exploring and collecting architectural data, meteorological data, and energy consumption data; Data Processing, regarding the transforming energy consumption data of residential housing and reference input data to make master data, which is analysis data that have been processed by filtering, refining, and type conversion of the collected data, for the big data analysis; Data Analytics, development of an analysis model for the energy consumption of residential housing applying analysis algorithm; Evaluation, data assessment and application of the analytical model.The purpose of this study is to reproduce green remodeling with useful information: analysing a variety of data open to the private sector using big data analysis techniques. It is expected that the “Big Data Analysis Process for Energy Consumption” will be used to confirm the correlation between the energy consumption of residential houses and the architectural elements, and to effectively derive the energy performance improvement factors for energy saving in buildings.

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A Study on the Use of Outdoor Temperature Information for Evaluating Building Energy Savings

Jung¹

2021

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A Study on Classifying Building Energy Consumption Pattern Using Actual Building Energy Data

Cited by 4 publications

References 7 publications

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

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

A Study on the Use of Outdoor Temperature Information for Evaluating Building Energy Savings

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