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    A Comparative Study of Artificial Neural Network and ANFIS for Short Term Load Forecasting
    (IEEE, 2014) Cevik, Hasan Huseyin; Cunkas, Mehmet
    Short term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012.
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    Day Ahead Wind Power Forecasting Using Complex Valued Neural Network
    (IEEE, 2018) Cevik, Hasan Huseyin; Acar, Yunus Emre; Cunkas, Mehmet
    Wind power forecast is one of the daily processes performed by Wind Power Plants (WPPs). It is very important to provide the generation-consumption balance one-day in advance for electric power system. In this study a day ahead wind power forecast in hourly bases is carried out for seven WPPs. The data used in this forecast is composed of the generation data of seven WPPs and the numerical weather forecasts of these WPP site. While the train data consist of 12-month data, the test data consist of 6-month data. Complex Valued Neural Network (CVNN), a special kind of artificial neural network (ANN), are preferred as the forecast method and compared with Real Valued Neural Network (RVNN). While hour, wind speed forecasts and wind direction forecasts are used as the system inputs, the output is forecasted wind power. Since the CVNN works with complex number, the non-complex inputs are converted to complex values. Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Square Error (NRMSE) are preferred to show the forecast accuracy. While RVNN has an average of 12.82% NMAE and 16.8% NRMS, CVNN has 11.75% NMAE and 15.77% NRMSE. It is seen that CVNN method is more successful with the lower error rates than RVNN. Therefore, CVNN can be used as an effective tool for wind power forecast.
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    Forecasting Hourly Electricity Demand Using a Hybrid Method
    (IEEE, 2017) Cevik, Hasan Huseyin; Harmanci, Huseyin; Cunkas, Mehmet
    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.
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    A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods
    (ELSEVIER, 2019) Cevik, Hasan Huseyin; Cunkas, Mehmet; Polat, Kemal
    In this study, a new forecast model consist of three stages is proposed for the next hour wind power. In the first stage, wind speed, wind direction, and wind power have been forecasted by using historical data. Artificial Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Support Vector Regression (SVR) have been chosen as forecast methods, while Empirical Mode Decomposition (EMD) and Stationary Wavelet Decomposition (SWD) methods have been preferred as pre-processing methods. The other two stages have been used to improve the wind power forecast value obtained at the end of the first stage. In the second stage, the forecast values found in the first stage have been applied to the same forecast methods, and wind power forecast value has been updated. In the third stage, a correction process is applied, and the final forecast value is obtained. While four-year data are selected as train data, two-year data are tested. SWD-ANFIS has given the best results in the first stage while ANN has given the best result in the second stage. Finally, the ensemble result has been found by taking the weighted average of the results of the three methods. Mean Absolute Error (MAE) values found at each stage are the 0.333, 0.294 and 0.278, respectively. The obtained results have been compared with literature studies. The results show that the proposed multistage forecast model is capable of wind power forecasting efficiently and produce very close values to the actual data. (C) 2019 Elsevier B.V. All rights reserved.
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    Short-term load forecasting using fuzzy logic and ANFIS
    (SPRINGER LONDON LTD, 2015) Cevik, Hasan Huseyin; Cunkas, Mehmet
    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.