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Customer Baseline Load Calculation using Time Series Prediction Technique in Energy Efficiency Programs

시계열 모델을 이용한 행동기반 에너지 효율화 프로그램의 고객기준부하 산정 방안

  • Koh, Sae-Hyun (School of Electrical Engineering, Korea University) ;
  • Joo, Sung-Kwan (School of Electrical Engineering, Korea University) ;
  • Lee, Jae-Hee (Dept. of Information and Electronic Engineering, Mokpo National University) ;
  • Moon, Guk-Hyun (KEPCO KEMRI) ;
  • Wi, Young-Min (School of Electrical and Electronic Engineering, Gwangju University)
  • Received : 2018.07.04
  • Accepted : 2018.12.11
  • Published : 2019.01.01

Abstract

As global demand for energy, energy prices, and power generation has increased worldwide, the government is turning to supply-oriented electricity supply and demand policies, such as behavior-based energy efficiency programs. In order to measure the implementation effect of the behavior-based energy efficiency program, the energy reduction must be accurately calculated by calculating the customer baseline load.

Keywords

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그림 1 시계열 예측 기법 과정 Fig. 1 Time series forecasting process

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그림 2 민감도 추정 방식 (더운 구간) Fig. 2 Sensitivity estimation process (Hot Section)

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그림 3 기준 온도 설정 알고리즘 Fig. 3 Algorithm for Standard Temperature

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그림 4 모델 1-6 CBL과 실적 비교 Fig. 4 CBL vs. actual value (Model 1-6)

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그림 5 2016년 1월-8월 CBL 모델별 평균 오차율 Fig. 5 Average MAPE of each CBL model

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그림 6 특정 가구의 2013-2016년 전력사용 패턴 Fig. 6 Monthly consumption pattern of a customer (2013~2016)

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그림 7 CBL 모델별 오차율의 표준편차 Fig. 7 Standard deviation of error rate of CBL models

표 1 공용주택 단지 전력사용량 정보 Table 1 Electricity consumption of apartment complex

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표 2 시계열 예측 기법 모델의 종류 Table 2 Types of time series prediction model

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표 3 온도 조건이 보정되지 않은 시계열 모델 Table 3 Models without adjusting temperature condition

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표 4 2016년 1월-8월 CBL 모델별 오차율 (%) Table 4 MAPE of each CBL model (2016 JAN-AUG)

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표 5 공용주택 단지 연도별 전력사용량 평균값 Table 5 Annual average electricity consumption of apartment complex

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