Title: Natural gas demand forecasting based on a subdivided forecasting model and rule-based calibration

Authors: Gisun Jung; Jinsoo Park; Young Kim; Yun Bae Kim

Addresses: Department of Industrial Engineering, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongno-gu, Seoul, South Korea ' Department of Management Information Systems, Yongin University, 134 Yongindaehak-ro, Samga-dong, Cheoin-gu, Yongin-si, Gyeonggi-do, South Korea ' Department of Industrial Engineering, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongno-gu, Seoul, South Korea ' Department of Systems Management Engineering, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongno-gu, Seoul, South Korea

Abstract: In South Korea, with growing volatility in natural gas demand owing to the implementation of eco-friendly energy policies, accurate demand forecasting is becoming more essential. Natural gas demand in South Korea is divided into city and power generation gas. To forecast the volatile energy demand considering daily and regional characteristics, detailed mathematical models and rules to calibrate subtle variations are needed. Power generation gas is more difficult to predict because of exceptional conditions changing the demand pattern owing to sudden weather changes. We propose a subdivided mathematical model that reflects use and daily and regional characteristics. Additionally, adopting rule-based calibration improved forecasting accuracy compared with using only the mathematical model. We performed a forecasting test for one year and confirmed that the average error rate was approximately 2.9%, a substantial reduction in mean absolute percentage error (MAPE) compared to the previously employed moving average method, which validates our proposed method. [Received: October 26, 2021; Accepted: August 13, 2022]

Keywords: demand forecasting; rule-based calibration; time series; energy operation; natural gas.

DOI: 10.1504/IJOGCT.2023.129575

International Journal of Oil, Gas and Coal Technology, 2023 Vol.32 No.4, pp.374 - 391

Received: 26 Oct 2021
Accepted: 13 Aug 2022
Published online: 14 Mar 2023 *

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