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An Electricity Energy and Water Consumption Model for Korean Style Apartment Buildings
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- Authors
- Dongjun Suh, Yoon-Sik Yoo, Il-Woo Lee, Seongju Chang
- Issue Date
- 2012-10
- Citation
- International Conference on Control, Automation and Systems (ICCAS) 2012, pp.1113-1117
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This research focuses on building-information related energy consumption estimation modeling by analyzing domestic electricity energy and water consumption data. We identify the elements of apartment buildings which are related to electricity and water use and demonstrate energy consumption forecast based on the proposed model. To verify the patterns of electricity energy and water consumption in relation with apartment buildings, we collect data for one of the apartment complexes in South Korea. Both weather history data including temperature and relative humidity and building information such as floor number and the gross area of apartment units are selected as Building-Information Factors (BIF) to analyze their effects on energy use in apartment buildings. In this paper, we cover annual electricity energy and water consumption among major household utilities that is manipulated by BIF. We conduct Multiple Regression Analysis (MRA) and use neural network-based model such as Multiple-Layer Perceptron (MLP) not only to reveal the relation among the elements such as weather information and BIF toward energy and water consumption model but also to estimate electricity energy and water use forecast. By carrying out this research, the relationship among BIF and energy consumption data sets is turned out to be essential information for establishing a Korean Style Apartment Management Information System as well as establishing a real estate policy on housing prices according to the diversification in floor numbers and gross areas of apartment units.
- KSP Keywords
- Apartment buildings, Data sets, Energy and water, Energy consumption estimation, Energy use, History data, Housing prices, Information systems(IS), Management Information System, Multi-layer Preceptron(MLP), Multiple regression analysis