• Title/Summary/Keyword: Residential use profile

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Prediction of Electrical Load Profile for Use in Simulating the Performance of Residential Distributed Generation Systems (가정용 분산전원시스템의 성능 모사를 위한 전력부하 프로파일 예측)

  • Lee, Sang-Bong;Cho, Woo-Jin;Lee, Kwan-Soo
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.23 no.4
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    • pp.265-272
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    • 2011
  • The electrical load profiles of end-users must be analysed properly to introduce distributed generation system efficiently. In this study, numerical simulation for predicting a residential electrical load profile was developed to satisfy categorized electricity consumption range. We applied bottom-up approach to compose electrical load profile by using data from official reports and statistics. The electrical load profile produced from the simulation predicted peak times of public report accurately and agreed well with the standard residential electrical load profile of official reports within average error of 16.2%.

Spatial Distribution of Temperature in and around Urban Parks- A Case Study of around Changkyeong Palace, Changdeok Palace and Jongmyo in Seoul- (도시 녹지와 그 주변 기온의 공간적 분포- 서울시 종로구 창경궁, 창덕궁, 종묘 주변을 사례로-)

  • 권영아;이현영
    • Journal of the Korean Geographical Society
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    • v.36 no.2
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    • pp.126-140
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    • 2001
  • The influence of small urban parks(green ratio is 100%) on the temperature pattern over the urban and its surrounding area was examined by analyzing the case of in and around Changkyeong palace, Changdeok plalace and Jongmyo, Jongro-gu, Seoul. The pattern of temperature over urban parks and their surrounding built-up area was analyzed from September to November 2000, measuring maximum and minimum temperatures with fixed sensors(maximum and minimum thermometer)and real-time temperature depends largely on both the land-use type and the distance from the park border. In the case of maximum temperature, the lowest value appeared on the green area within parks and the highest value on the built-up area far from the green area. The maximum temperature difference between parks and built-up areas was up to $7.3^{\circ}C$. In the built-up area, the maximum temperature of commercial areas was higher than residential areas. In the night time, not only land-use type but also topography is important for the spatial distributlon of temperature because of the cold airflow from adjacet hills. The horizontal temperature profile by mobile measurement is also related to land-use type and to the distance from the park borders. There is a magnitude of $1^{\circ}C$ temperture difference over a distance of 200m and $3~4^{\circ}C$ over a distance of 400m from the park borders.

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UX Methodology Study by Data Analysis Focusing on deriving persona through customer segment classification (데이터 분석을 통한 UX 방법론 연구 고객 세그먼트 분류를 통한 페르소나 도출을 중심으로)

  • Lee, Seul-Yi;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.27 no.1
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    • pp.151-176
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    • 2021
  • As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.