Scopus
YÖKSİS Eşleşti
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
DOKTOR ÖĞRETİM ÜYESİ HASAN HÜSEYİN ÇEVİK →
Short term load forecasting using fuzzy logic and ANFIS
Neural Computing and Applications · 2015 SCI-Expanded
PROFESÖR MEHMET ÇUNKAŞ →
Makale Bilgileri
DergiNeural Computing and Applications
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
Metrikler
128
Atıf
2
Yazar
4
Anahtar Kelime