지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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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.
목차
1. 서론
2. 도시가스 일일 공급량 데이터 분석
3. 도시가스 수요예측 모형의 개발
4. 모형 적용 결과 및 분석
5. 결론
참고문헌
참고문헌 (19)
참고문헌 신청
1. [학술지(정기간행물)] - Amina, M / 2012 / A hybrid intelligent approach for the prediction of electricity consumption / International Journal of Electrical Power and Energy Systems 43 (1) : 99 ~ 108
2. [학술지(정기간행물)] - Azadeh, A / 2010 / An adaptive network-based fuzzy infrerence system for short-term natural gas demand estimation : Uncertain and complex environments / Energy Policy 38 (3) : 1529 ~ 1536
3. [학술지(정기간행물)] - Baczyński, D / 2004 / Influence of artificial neural network structure on quality of short-term electric energy consumption forecast, IEE Proceedings-Generation / Transmission and Distribution 151 (2) : 241 ~ 245
4. [학술지(정기간행물)] - 백종관 / 2011 / 한국지역난방공사의 겨울철 열수요 예측을 위한 선형회귀모형 개발 / 한국산학기술학회논문지 12 (3) : 1488 ~ 1494
5. [학술대회논문] - Brown, R. H / 1995 / Development of artificial neural-network models to predict daily gas consumption / Proceedings of the 1995 IEEE IECON 21st International Conference on Industrial Electronics, Control, and Instrumentation : 1389 ~ 1394
2. [학술지(정기간행물)] - Azadeh, A / 2010 / An adaptive network-based fuzzy infrerence system for short-term natural gas demand estimation : Uncertain and complex environments / Energy Policy 38 (3) : 1529 ~ 1536
3. [학술지(정기간행물)] - Baczyński, D / 2004 / Influence of artificial neural network structure on quality of short-term electric energy consumption forecast, IEE Proceedings-Generation / Transmission and Distribution 151 (2) : 241 ~ 245
4. [학술지(정기간행물)] - 백종관 / 2011 / 한국지역난방공사의 겨울철 열수요 예측을 위한 선형회귀모형 개발 / 한국산학기술학회논문지 12 (3) : 1488 ~ 1494
5. [학술대회논문] - Brown, R. H / 1995 / Development of artificial neural-network models to predict daily gas consumption / Proceedings of the 1995 IEEE IECON 21st International Conference on Industrial Electronics, Control, and Instrumentation : 1389 ~ 1394
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UCI(KEPA) : I410-ECN-0101-2014-500-002954010