• 대한산업공학회지
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• 제39권4호
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• 한국산업정보학회논문지
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• 제18권3호
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• pp.35-46
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• 2013

국내 전력 수요량 예측을 위한 정확한 분석 모델을 개발하기 위하여 고차원 데이터 군집 분석에 적합한 차원 축소 개념의 부분공간 군집 기법과 SMO 분류 기법을 결합한 전력 수요 패턴 예측 방법을 제안하였다. 전력 수요 패턴 예측은 무선부하감시 데이터 뿐 아니라 소지역 단위의 센서스 정보를 통합하여 시간대별 전력 부하 패턴 분석과 인구통계학 및 지리학적 특성 분석이 가능하다. 서울지역 대상의 센서스 정보 및 전력 부하를 이용한 소지역 전력 수요 패턴 예측 결과 총 18개의 특성 군집을 구성하였으며, 전력 수요 패턴 예측 정확도는 약 85%를 보였다.

• 한국수소및신에너지학회논문집
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• 제33권5호
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• 한국산학기술학회논문지
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• 제13권8호
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• pp.3307-3314
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• 2012

농어촌 주택 표준모델에 대한 연간 건물에너지요구량을 DesignBuilder를 사용하여 계산하였다. 실내설정온도, 외벽의 열관류율, 창호타입, 환기량을 변화시키며 건물의 설계/운영 변수들이 에너지요구량에 미치는 영향을 분석하였다. 건물의 크기와 상관없이 난방에너지요구량이 냉방에너지요구량보다 더 큰 것으로 나타났다. 단열재 두께를 증가시켜 외벽의 열관류율을 감소시키면 단열효과의 증가로 난방에너지요구량은 감소하지만 냉방에너지요구량이 증가한다. 하지만 외벽의 열관류율 조절을 통한 난방에너지요구량의 절감에는 한계가 있으므로 외부에 직접 노출된 외벽의 면적을 최소화하는 것이 더 효과적인 것으로 판단된다. 계산에 사용된 4가지 창호 중 일반 이중창호가 난방에너지요구량 측면에서 가장 유리한 것으로 나타났다. 침기량은 일정하게 유지하고 단위시간당 환기량을 증가시킨 경우 냉방에너지요구량은 큰 변화가 없었으나 난방에너지요구량이 증가하였다. 실내의 공기질이 만족되는 범위 내에서 환기량을 최소화하는 것이 연간 건물에너지요구량을 줄이는데 유리한 것으로 판단된다.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• 문화기술의 융합
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• 제10권4호
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• pp.693-700
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• 2024

에너지 소비 문제가 전 세계적으로 주요한 이슈로 자리잡아 다양한 부문에서 에너지 소비 및 온실가스 배출 절감에 대한 관심이 크다. 2022년 3월 말 기준 국내 산업단지 총 면적은 606 km2로, 전체 국토면적의 약 0.6 %에 불과한다. 하지만 2018년 기준, 국내 산업단지의 연간 에너지 사용량은 국가 전체 에너지 사용량의 53.5 %, 전체 산업부문 에너지 사용량의 83.1 %를 차지하는 110,866.1천 TOE임으로 확인되었다. 더불어 국가 전체 온실가스 배출량의 45.1 %, 산업부문 온실가스 배출량의 76.8 %를 차지하여 환경에 미치고 있는 영향 또한 상당한 상황임이 확인하였다. 이러한 배경 하에 본 연구에서는 산업단지 차원의 에너지 효율화에 기여하고자, 국내 한 산업단지를 대상으로 에너지 수요 및 공급의 예측을 진행하였으며, 예측 결과값을 포함하여 에너지 모니터링을 위한 시뮬레이터 UI 화면을 설계하였다. 머신러닝 알고리즘 중 다층퍼셉트론 (Multi-Layer Perceptron; MLP)을 사용하였으며, 예측 모델의 최적화 기법으로서 베이지안 최적화 (Bayesian Optimization)를 적용하였다. 본 연구에서 구축한 예측 모델은 산업단지 내 압축공기 수요 유량의 경우는 87.90 %, 공용 공기압축기 공급 가능 유량의 경우는 99.54 %의 예측 정확도를 보였다.

• Industrial Engineering and Management Systems
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• 제13권3호
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• pp.319-329
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• 2014

Supply chain management is a subject that has an increasing importance due to the developments in the global markets and technology. In this paper, a fuzzy multi-objective linear programming model is developed for the supply chain of a company dealing with procurement, storage, filling, and distribution of liquefied petroleum gas (LPG) in Turkey. The model intends to determine the quantities of LPG to be procured, stored, filled to cylinders, and transported between the plants and demand centers for six planning periods. In this model, which aims to minimize both total costs (sum of procurement, storage, filling, and transportation costs) and total transportation distances, demand quantities of the main demand centers and decision maker's aspiration levels about objective functions are fuzzy. After comparing the results obtained from the model with those obtained by using different methods, it is concluded that the proposed method can be applied to real world problems practically and it may be used in this type of problems in order to generate an efficient solution.

• 산업공학
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• 제9권3호
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• pp.64-71
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• 1996

In the survivability and simplicity aspect, SONET Self-healing Ring(SHR) is one of the most important schemes for the high-speed telecommunication networks. Since the ring capacity requirement is defined by the largest STS-1 cross-section in the ring, load balancing is the key issue in the design of SONET SHR. Recently, most of the research on load balancing problem have been concentrated on the SONET single-ring case. However, in certain applications, multiple-ring configuration is necessary because of the geographical limitations or the need for extra bandwidth. In this paper, the load balancing problem for SONET dual-ring is considered by assuming symmetric inter-ring demands. We present a linear programming based formulation of the problem. Initial solution and improvement procedures are presented, which solves the routing and interconnection between the two rings for each demand. Computational experiments are performed on various size of networks with randomly generated demand sets. Results show that the proposed algorithm is excellent in both the solution quality and the computational time requirement. The average error bound of the solutions obtained is 0.26% of the optimum.

• 산업경영시스템학회지
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• 제34권4호
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• pp.120-128
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• 2011

Recent development of communication technology and evolution of telecommunications services, various needs of users are giving Telecom operators difficulties in discovering new services and its promotion strategies. In addition, at the launching of new services, if carriers does not consider complementary and substitution relationships with competing services or self-encroachment problem with the existing service profitability is reduced or lost in the current market. In this study, by analyzing usage pattern of telecommunications services respect to changes in supply and demand environment, the complementarity/substitutability analysis and systematic analytical framework is proposed. This study proposes approaches to establish mobile operators' business strategies and to identify new services demand.

• 한국산학기술학회논문지
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• 제16권7호
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• pp.4489-4497
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• 2015

본 연구는 말고기에 대한 소비자 수요와 지불의사액을 추정하는데 연구목적이 있다. 먼저 말고기에 대한 소비자 수요를 현재수요(current demand), 잠재추가수요(potential demand) 그리고 잠재신규수요(latent demand)로 구분하여 조사하였으며 로짓 모형을 이용하여 소비자 수요별 설명변수와의 유의성을 검토하였다. 다음으로 이선선택형 가상가치법(DC-CVM)을 이용하여 말고기에 대한 지불의사액을 추정하였다. 말고기에 대한 소비자 지불의사액은 한우 3등급 등심부위 가격의 67.8% 수준이었으며, 이들 지불의사액에 미치는 영향은 남자일수록, 연령이 낮을수록, 그리고 말고기에 대한 인지도가 높을수록 지불의사액이 높았다.