caa a22 4500 606159150 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-014-1809-4 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1809-4 Short-term load forecasting using fuzzy logic and ANFIS [Elektronische Daten] [Hasan Çevik, Mehmet Çunkaş] 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 1year. This paper shows that fuzzy logic can give good results in very large test data sets for 1year. 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. The Natural Computing Applications Forum, 2014 Short-term load forecasting nationallicence Forecast methods nationallicence Fuzzy logic nationallicence ANFIS nationallicence Çevik Hasan Department of Electrical and Electronics Engineering, Technology Faculty, Selcuk University, 42003, Konya, Turkey aut Çunkaş Mehmet Department of Electrical and Electronics Engineering, Technology Faculty, Selcuk University, 42003, Konya, Turkey aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1355-1367 0941-0643 26:6<1355 2015 26 521 https://doi.org/10.1007/s00521-014-1809-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1809-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Çevik Hasan Department of Electrical and Electronics Engineering, Technology Faculty, Selcuk University, 42003, Konya, Turkey aut NATIONALLICENCE 700 1- Çunkaş Mehmet Department of Electrical and Electronics Engineering, Technology Faculty, Selcuk University, 42003, Konya, Turkey aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1355-1367 0941-0643 26:6<1355 2015 26 521