Earticle

집계화 수준과 자료수집 주기가 상이한 시계열자료를 결합하여 짧은 주기의 일관성있는 시계열자료를 구축하는 기법을 제시하였다. 추정과 예측을 확장 회귀식의 추정으로 단일화한 Salkever(1971)의 기법을 활용하여, 자료의 일관성 확보문제를 확장 회귀식체계 하에서의 상이한 확장 회귀식에 등장하는 모수들간의 제약으로 전환할 수 있었다. 따라서, SURE형태의 확장 회귀식체계에 반복 GLS 추정기법을 적용하여 짧은 주기의 일관성있는 시계열자료를 얻을 수 있었다. 본 논문의 기법을 적용하여, 집계화 수준과 자료수집 주기가 상이한 [에너지총조사]와 [에너지통계연보]의 시계열자료를 결합하여 석유화학(대분류부문)에 속하는 3개 중분류부문에 대한 연간 시계열자료를 구축하였다.

We propose an econometric method to construct a logically- consistent time series from multiple data sources with differences in sector aggregation and survey periodicity. Logical consistency is the requirement that at each period the total of the predicted values of component subsectors should be equal to the actual value of the integrating sector. Building upon Salkever (1971), a consistent time series is constructed by applying iterative GLS to a system of augmented regression equations with cross-equation restrictions on the parameters. We successfully applied our method to the construction of annual data for the three subsectors of Petrochemicals by combining annual data (from Yearbook of Energy Statistics) on Petrochemicals and triennial data (from Energy Consumption Survey) on its three subsectors.

Energy industry is undergoing a paradigm shift in customer participation in the smartgrid. Customers traditionally consume electrical power. But nowadays not only do they generate electricity from private distributed generations, they can participate in demand response programs with their negawatt power which means a theoretical unit of power representing an amount of energy saved. Therefore development of decision-making criteria for electric load aggregation becomes a greater consideration as an amount of energy saved from demand response resources increases. This paper proposes load aggregators' decision-making criteria in the industrial sector where it made up the largest portion in demand response portfolio in order to assure reliability performance for demand response resources.