기존 건물 에너지 데이터 분석을 통한 원격 에너지진단 알고리즘 성능 검증 연구

다국어 초록 (Multilingual Abstract)

Energy reduction and efficiency in existing buildings are an essential part of energy saving in building sector. Measurement of energy consumption by end-use and analysing data to find opportunities in improvement of energy performance are the key steps and the most important part of energy saving activities for buildings.
This paper introduces and studies a recently-developed remote energy diagnosis program which has a new approach in building energy audit and analysis processes. The program consists of two algorithms - disaggregation and prediction. The first one is an algorithm for disaggregation of electricity consumption by end-use which divides the actual whole electricity consumption data of a building into three different end-use loads - heating & cooling load, lighting & other power supply load, and base load. Building characteristic factors in both heating and cooling periods are calculated for each building in this algorithm. The second algorithm provides the prediction of weather-corrected electricity consumption of a whole building. The output of the first algorithm and present outdoor temperature are combined for the prediction of present normal electricity consumption.
This program does not require any difficult-to-get information or any hardwares for measurement. The analysis procedure is a lot quicker than the traditional on-site diagnosis approach. The validation of this remote energy diagnosis program is made based on comparison with actual building electricity consumption data. The result shows that the mean bias error(MBE) is -0.12% and coefficient of variation of the root mean square error(CV) is 5.7%, which are much better results than ASHRAE standards of acceptable calibration tolerances of ±5% and 30% respectively.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 선행연구 - 건물에너지관리와 관련된 예비적 고찰
• 3. 원격 에너지진단 프로그램
• 4. 결론
• Reference

참고문헌 (Reference)

1 정희규, "기존 건물의 에너지 개수방법의 제안 및 사례연구" 대한설비공학회 24 (24): 663-670, 2012

2 Moon, H, "Recent Research Trends in Building Energy Management System" 42 (42): 54-63, 2013

3 KEEI, "Monthly Energy Statistics"

4 Cho, Y, "In Small and Medium Business the Government 3.0-based Big Data Utilization Policy" 3 (3): 15-22, 2013

5 손학식, "BEMS(Building Energy Management System) 구축을 위한 주요 구성요소와 건물에너지효율등급 개선효과에 관한 연구" 한국조명.전기설비학회 28 (28): 105-113, 2014

6 Kim, D, "An Analysis of Import and Revitalization of the Building Energy Management System for Energy-saving about Application case of the BEMS in Japan" 28 (28): 571-574, 2008

7 ASHRAE Guideline, "ASHRAE Guideline 14-2002, Measurement of Energy Demand Savings, SXection 6.3.3.4.2.2"

8 Seol, I, "A Study of the Current Status of Domestic Building Energy Management System and the Correct way for Improvement" 58-63, 2012

9 Seo, D, "A Study for Predicting Energy Use in an Office Building" 1309-1316, 1998

10 Leigh, S, "A Study for Predicting Building Energy Use with Regression Analysis" 12 (12): 1090-1097, 2000