Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Short-term load forecasting using fuzzy logic and ANFIS
Scopus
Toplam 141 atıf DOI
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.
Atıf Kaynağı
Atıf Yapan Yayın
Forecasting hourly electricity demand using a hybrid method
Scopus
Havuzumuzda 4 atıf almış
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In this study, a short-term electricity load forecast is done for 24 hours of next day. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) techniques are used for the forecast method in a hybrid form. The weights of ANN is updated by PSO in learning phase. Historical load consumption data, historical daily mean air temperature data and season are selected as inputs. Load data of 4 years on hourly basis are taken into account. Train and test data are considered as 3 years and 1 year, respectively. The MAPE error is found as 2.15 for one year period on an hourly basis.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Selçuk Üniversitesi
Selçuklu, Turkey
Yozgat Bozok University
Yozgat, Turkey