Energy Performance Assessment According to Data Acquisition Levels of Existing Buildings

Kim, Sun Sook

doi:10.3390/en12061149

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…Actually, it is an issue facing many countries that are performing benchmarking [3][4][5]. Thus, to develop a usable benchmark, it is important to check the state of the collected data [3,[6][7][8][9]. In addition, the benchmarking information must be communicated to stakeholders in an appropriate way [10].…”

Section: Introductionmentioning

confidence: 99%

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Kim

2020

Energies

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In an effort to improve the energy efficiency of existing buildings, it is necessary to first evaluate the energy performance of those buildings. Since it is difficult to obtain detailed information on existing buildings, the challenge is how to conduct reliable energy performance assessments with this limited information. As a result, many countries have adopted evaluation systems based on measured energy consumption data for existing buildings. This study aims to analyze the building energy consumption and characteristics using Korea’s national building database and provide an energy performance benchmark for continuous management of the energy performance of existing buildings. We analyzed the relationship between the basic statistical characteristics of the information collected from the national integrated energy database and energy consumption. The total floor area was found to be closely related to energy consumption, and various regression analysis methods were applied and compared to develop a benchmark to explain the trends of energy consumption according to the increase in total floor area. Finally, the developed benchmarks were used to evaluate energy consumption and examine the feasibility of the benchmarks.

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Section: Introductionmentioning

confidence: 99%

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Kim

2020

Energies

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“…The procedure chosen to carry out the model validation was based on the Hwa Cho and Sook Kim methodology [24] simplified in Figure 1. The process started with dwelling selection to continue with a detailed description through an audit evaluation that allowed the creation of the theoretical model (TM).…”

Section: Methodsmentioning

confidence: 99%

Indoor Temperature Validation of Low-Income Detached Dwellings under Tropical Weather Conditions

Barrientos-González

Vega-Azamar

Cruz

et al. 2019

Climate

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Urban territorial expansion generated in the last decades has brought a series of consequences, such as the variation between urban and suburban weather conditions affecting indoor temperature and increasing electricity consumption derived from the use of cooling systems. Current approaches of simulation models in residential buildings use indoor environmental data for carrying out validations to propose hygrothermal comfort alternatives for the mitigation of the effects of the external environmental conditions on the interior spaces of dwellings. In this work, an hourly evaluation of both indoor and outdoor environmental parameters of two case studies in a tropical climate was carried out, by means of a whole-building simulation approach tool during a week representative of the warmest period of the year. The integration of the collected environmental data in the theoretical model allowed us to reduce the error range of the estimated indoor temperature with results in normalized mean bias error between 7.10% and −0.74% and in coefficient of variation of the root mean square error between 16.72% and 2.62%, in the different indoor zones of the case studies. At the same time, the energy assessment showed a difference of 33% in Case 1 and −217% in Case 2 for final electricity consumption.

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“…Alternatively, when a simplified building model handles incomplete data, its findings are usually fairly robust and efficient [25]. However, the assessment methodology has depended on the situation in each specific area, and it can be difficult to generalize [26].…”

Section: Necessities and Challenges Of Building Performance Modeling mentioning

confidence: 99%

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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Energy Performance Assessment According to Data Acquisition Levels of Existing Buildings

Cited by 14 publications

References 28 publications

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Indoor Temperature Validation of Low-Income Detached Dwellings under Tropical Weather Conditions

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

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