Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Short-term load forecasting using fuzzy logic and ANFIS
Neural Computing and Applications · Ağustos 2015
YÖKSİS Kayıtları
Short term load forecasting using fuzzy logic and ANFIS
Neural Computing and Applications · 2015 SCI-Expanded 1 atıf
Dr. Öğr. Üyesi HASAN HÜSEYİN ÇEVİK →
Short term load forecasting using fuzzy logic and ANFIS
Neural Computing and Applications · 2015 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
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2015 ISSN: 0941-0643 SCI-Expanded 1 atıf
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Short term load forecasting using fuzzy logic and ANFIS
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2013 ISSN: 0941-0643 SCI
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2018 ISSN: 0941-0643 SCI-Expanded Q1
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2016 ISSN: 0941-0643 SCI-Expanded
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2017 ISSN: 0941-0643 SCI-Expanded
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2016 ISSN: 0941-0643 SCI-Expanded
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Dr. Öğr. Üyesi GÜZİN ÖZMEN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
2013 ISSN: 0941-0643 SCI-Expanded 5 atıf
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2017 ISSN: 0941-0643 SCI-Expanded
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Makale Bilgileri
ISSN09410643
Yayın TarihiAğustos 2015
Cilt / Sayfa26 · 1355-1367
Scopus ID2-s2.0-84937966771
Özet
This paper presents short-term load forecasting models, which are developed by using fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS). Firstly, historical data are analyzed and weekdays are grouped according to their load characteristics. Then, historical load, temperature difference and season are selected as inputs. In general literature, fuzzy logic hourly load forecasts are tested in the range a few days or a few weeks. Unlike previous studies, the hourly load forecast is carried out for 1 year. This paper shows that fuzzy logic can give good results in very large test data sets for 1 year. Besides, for countries with large areas, the temperature data taken from only one point would lead to increase the forecasting errors. Therefore, the average of temperature for six cities having the maximum power consumption is weighted average. The mean absolute percentage errors of the fuzzy logic and ANFIS models in terms of prediction accuracy are obtained as 2.1 and 1.85, respectively. The results show that the proposed fuzzy logic and ANFIS models are capable of load forecasting efficiently and produce very close values to the actual data and are the alternative way for short-term load forecasting in Turkey.
Yazarlar (2)
1
Hasan Hüseyin Çevik
2
Mehmet Çunkaş
Anahtar Kelimeler
ANFIS
Forecast methods
Fuzzy logic
Short-term load forecasting
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
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Scimago Dergi (ISSN Eşleşmesi)
Neural Computing and Applications
Q1
SJR Skoru1,102
H-Index146
YayıncıSpringer London
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Software (Q1)
Metrikler
140
Atıf
2
Yazar
4
Anahtar Kelime