A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption

Hong, Taehoon; Koo, Choongwan; Park, Sung-Ki

doi:10.1016/j.buildenv.2012.01.001

Cited by 56 publications

(18 citation statements)

References 29 publications

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“…There are many studies on gas and energy consumption issues that consider the major characteristics of apartment buildings [2][3][4][5][6][7][8][9][14][15][16][17][18][19][20][21][22][23]. Analyzing the energy sensitive factors of apartment buildings, including the facility and building features, are investigated with a variety of constituents.…”

Section: Background Work For Parametric Studymentioning

confidence: 99%

“…Because Korean government requires U-value of 1.5 W/m 2 · K [5], double and triple low-e glazing that satisfy this criterion, are selected. For varying total thermal conductivity through glazing, the range from single clear glazing all the way up to triple low-e glazing is selected to investigate the energy sensitivity of different glazing types.…”

Section: Window Typementioning

confidence: 99%

“…Because the Korean government requires a U-value of 1.5 W/m 2 · K [5], double and triple low-e glazing that satisfy this criterion were selected as shown in Tables 4 and 5. Six window types were simulated in an iterative test to investigate the effect of combinations of glazing layers on energy load.…”

Section: Glass Type (T7)mentioning

confidence: 99%

“…As the Korean government has pursued reducing residential energy use [5][6][7], many researchers have studied and derived residential energy consumption models to construct energy efficient apartment housings in Korea. Most of the studies related to energy consumption in Korean housing focus on energy consumption patterns with respect to identifying each parameter's potential impact on energy performance.…”

Section: Introductionmentioning

confidence: 99%

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A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

Suh

Chang

2014

Energies

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This research presents an evaluative energy model for estimating the energy efficiency of the design choices of architects and engineers in the early design phase. We analyze the effects of various parameters with different characteristics in various combinations for building energy consumption. With this analysis, we build a database that identifies a set of heuristic rules for energy-efficient building design to facilitate the design of sustainable apartment housing. Perturbation studies are based on a sensitivity analysis used to identify the thermal influence of the input design parameters on various simulation outputs and compare the results to a reference case. Energy sensitivity weight factors are obtained from an extensive sensitivity study using building energy simulations. The results of the energy sensitivity study summarized in a set of heuristic rules for evaluating architectural features are estimated through case studies of Korean apartment buildings. This study offers valuable guidelines for developing energy-efficient residential housing in Korea and will help architects in considering appropriate design schemes and provide a ready reference to generalized test cases for both architects and engineers so that they can zero in on a set of effective design solutions. OPEN ACCESSEnergies 2014, 7 6898

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Section: Background Work For Parametric Studymentioning

confidence: 99%

Section: Window Typementioning

confidence: 99%

Section: Glass Type (T7)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

Suh

Chang

2014

Energies

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“…In his book, Hand [17] demonstrated that the analysis of large observation datasets, using data mining methods, could reveal hidden data relationships, enabling the data to be summarized in novel ways which provided insight for decision making. In recent years, data mining has gained popularity in the building science area, including occupant behavior, fault detection [18], building automation systems [19] and building energy performance [20][21][22]. Data mining is an effective technique to gain new knowledge from big data, finding the new relevance from a nonobvious context.…”

Section: Introductionmentioning

confidence: 99%

Data mining of space heating system performance in affordable housing

Ren

Yan

Hong

2015

Building and Environment

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The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

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A Knowledge-Based Energy Management Model that Supports Smart Metering Networks for Korean Residential Energy Grids

Suh

Kim

2015

Wireless Pers Commun

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A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption

Cited by 56 publications

References 29 publications

A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

Data mining of space heating system performance in affordable housing

A Knowledge-Based Energy Management Model that Supports Smart Metering Networks for Korean Residential Energy Grids

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