An extreme learning machine based very short-term wind power forecasting method for complex terrain

ABSTRACT

In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is analyzed with Wind Atlas Analysis and Application Program (WAsP). According to the obtained RIX value, the terrain of WPP is found to be complex. Due to the complexity of the terrain, wind power forecasting becomes difficult. To deal with this problem, a forecasting method with fast, accurate, and high performance is needed. Therefore, Extreme Learning Machine (ELM) based method is proposed for wind power forecasting in this study. Electrical and meteorological measurements are obtained from WPP for the application of the proposed method. These measurements are provided with high quality measuring devices. Also, Global Positioning System (GPS) time synchronization is used to prevent lags between measurements. The wind speed, wind direction, and wind power data of 1-year period are obtained from WPP. These data are used to compare the proposed method with a classical Artificial Neural Network (ANN) based method in terms of two, three and four hours-ahead wind power forecast performances. In the forecast studies performed for all data related to 2, 3, and 4-hours ahead, Normalized Root Mean Square Error (NRMSE) values of ELM are obtained as 7.01, 10.12, and 12.06, respectively, while these values are found as 8.19, 12.18, and 13.09 for ANN. In addition, the values of Correlation Coefficients (R) of the proposed forecast method results regarding 2, 3, and 4-hours ahead are 0.96588, 0.93528, and 0.88984, respectively. The R values related to ANN are observed as 0.95421, 0.91373, and 0.87576, respectively. According to the obtained results, it is observed that ELM has better performance features than classic method under all forecast conditions and it is clearly seen that ELM has by far short training time than other one.

KEYWORDS:

• Short Term
• wind Power
• forecast
• extreme Learning Machine
• complex Terrain

Additional information

Notes on contributors

Hakan Acikgoz

Hakan Acikgoz received Ph.D degree in Electrical and Electronic Engineering from Kahramanmaras Sutcu Imam University in 2018. He is working as Assistant Professor in the Department of Electrical and Electronic Engineering at Gaziantep Islam Science and Technology University. He has ten years of work experience in the field of Academic. His research interests are power electronic converters, electronic power transformers, intelligent controllers and forecasting methods.

Ceyhun Yildiz

Ceyhun Yildiz received Ph.D degree in Electrical and Electronic Engineering from Kahramanmaras Sutcu Imam University in 2017. He is a lecturer in Department of Electricity and Energy at Kahramanmaras İstiklal University. His research interests are electrical machine drivers, renewable power, artificial intelligence and forecasting methods.

Mustafa Sekkeli

Mustafa Sekkeli received B.Sc., M.Sc. and Ph.D degrees in electrical and electronics engineering from Istanbul Technical University, 1986, 1989 and 2005 respectively. Between 1999 and 2007, He worked as a lecturer in Electrical and Electronics Department, Kahramanmaras Sutcu Imam University. Since 2007, he has been with Electrical and Electronics Engineering Department, Kahramanmaras Sutcu Imam University as Professor. His research interests include power quality, power electronics, electrical machine control, reactive power compensation, and renewable energy systems.