Abstract
Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecasting, power curves need to be analyzed and a forecasting method used that compensates for the variability of wind power outputs. In this paper, we analyzed the reliability of power curves at each wind speed using logistic regression. To reduce wind power forecasting errors, we proposed a short-term wind power forecasting method using support vector machine (SVM) based on linear regression. Support vector machine is a type of supervised leaning and is used to recognize patterns and analyze data. The proposed method was verified by empirical data collected from a wind turbine located on Jeju Island.
Highlights
Wind power generation has grown rapidly over the past few decades, as demonstrated by the total accumulated wind power capacity which hit 319 GW from an installation in 2013 [1]
Short-term of how much wind power can be expected at a specific time
To increase the penetration of wind wind of how much wind power can be expected at a specific time
Summary
Wind power generation has grown rapidly over the past few decades, as demonstrated by the total accumulated wind power capacity which hit 319 GW from an installation in 2013 [1]. Wind power outputs can be distributed (which do or do not enter the error range) at each wind speed, to create a probability model relating to the reliability of power curves. For wind power forecasting, there are models based on time series using auto regression moving average (ARMA)/auto regression integrated moving average (ARIMA), or the regression method [7]. We analyzed the accuracy of power curves at each wind speed using logistic regression, and proposed short-term wind power forecasting using support vector machine based on linear regression to reduce wind power forecasting errors. Mathematical Definition for Enhanced Reliability Assessments Method of Wind Turbines
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