Paper Info
Title
Proposal of the energy consumption analysis process for the residential houses using big data analytics technique
Abstract
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.
Year
DOI
Venue
2021
10.1093/jcde/qwab063
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
Keywords
DocType
Volume
big data, open data, residential houses, energy consumption, data collection, correlation analysis, data visualization
Journal
8
Issue
ISSN
Citations 
6
2288-4300
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
W. Pak100.34
Inhan Kim200.34
Jungsik Choi300.34