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Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

Author

Listed:
  • Mustafa Akpinar

    (Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, 54187 Sakarya, Turkey
    These authors contributed equally to this work.)

  • M. Fatih Adak

    (Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, 54187 Sakarya, Turkey
    These authors contributed equally to this work.)

  • Nejat Yumusak

    (Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, 54187 Sakarya, Turkey
    These authors contributed equally to this work.)

Abstract

The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC). For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE) for training utilizing MAPE with a univariate sliding window technique.

Suggested Citation

  • Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:781-:d:100628
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