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http://dx.doi.org/10.9708/jksci.2012.17.1.211

Building of Prediction Model of Wind Power Generationusing Power Ramp Rate  

Hwang, Mi-Yeong (Dept. of Computer Science, Chungbuk National University)
Kim, Sung-Ho (Dept. of Computer Science, Chungbuk National University)
Yun, Un-Il (Dept. of Computer Science, Chungbuk National University)
Kim, Kwang-Deuk (Korea Institute of Energy Research)
Ryu, Keun-Ho (Dept. of Computer Science, Chungbuk National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.1, 2012 , pp. 211-218 More about this Journal
Abstract
Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.
Keywords
Prediction accuracy; Prediction model; Wind power generation; Ramp; Power Ramp Rate(PRR);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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