Student:	Fatih ŞENOCAK
Supervisor:	Asst. Prof. Dr. Hakan KAHVECİ
Department:	Elektrik-Elektronik Müh.
Institution:	Graduate School of Natural and Applied Sciences
University:	Karadeniz Technical University Turkey

Title of the Thesis:	FORECASTING OF WEIGHTED AVERAGE ELECTRICITY MARKET CLEARING PRICE USING ARTIFICIAL NEURAL NETWORKS AND ANFIS
Level:	M.Sc.
Acceptance Date:	18/6/2018
Number of Pages:	98
Registration Number:	i3374

Summary:

In this study, an artificial neural network (ANN) and ANFIS model was established to

estimate the electricity market clearing price (MCL). Production quantities of primary

energy sources are used as input variables of the model. The input variables are modeled by

creating three categories. In the first category, the amount of production of each primary

energy source separately, in the second category, compounds of primary energy sources are

formed and their production quantities, in the third category, new partial MCLs are found by

multiplying each primary energy source of MCLs obtained by modifying each primary

energy source by the ratio of total production and a new MCL value is obtained by collecting

each partially obtained MCL. When these results are examined it appears that there is a close

connection between MCL and primary energy sources. With hydraulic primary energy

source, the right natural gas is inversely proportional to the primary energy source. When

the results of our models were compared, the most successful results were obtained within

the third category and MAPE error rates were found as 3.74% for ANFIS and 5.80% for

ANN. The fact that the results are close to reality will help to obtain more accurate results

in the electricity market clearing price forecasting models based on primary energy sources.

The accuracy of the results obtained from the model depends on the availability of primary

energy sources. Because of the resulting supply shortage or some primary energy resource

shortage, it has been shown that excessive increases in production adversely affect the

model, due to the fact that the other primary energy sources are being replaced.