Açık Akademik Arşiv Sistemi

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

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dc.contributor.authors Akpinar, M; Adak, MF; Yumusak, N;
dc.date.accessioned 2020-01-13T07:57:00Z
dc.date.available 2020-01-13T07:57:00Z
dc.date.issued 2017
dc.identifier.citation Akpinar, M; Adak, MF; Yumusak, N; (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, 10, -
dc.identifier.uri https://hdl.handle.net/20.500.12619/2468
dc.identifier.uri https://doi.org/10.3390/en10060781
dc.description.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.
dc.language English
dc.publisher MDPI
dc.subject Energy & Fuels
dc.title Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey
dc.type Article
dc.identifier.volume 10
dc.contributor.department Sakarya Üniversitesi/Bilgisayar Ve Bilişim Bilimleri Fakültesi/Yazılım Mühendisliği Bölümü
dc.contributor.saüauthor Akpınar, Mustafa
dc.contributor.saüauthor Adak, Muhammed Fatih
dc.contributor.saüauthor Yumuşak, Nejat
dc.relation.journal ENERGIES
dc.identifier.wos WOS:000404384000047
dc.identifier.doi 10.3390/en10060781
dc.identifier.eissn 1996-1073
dc.contributor.author Akpınar, Mustafa
dc.contributor.author Adak, Muhammed Fatih
dc.contributor.author Yumuşak, Nejat


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