Method for Predicting Wind Power Density
First Claim
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1. A method for predicting a wind power density using a stepwise regression analysis technique, configured in a form of a program executed by an execution processing means including a computer, comprising:
- a variable inputting step (S1) of inputting the wind power density, which is an output variable, and one or more input variables selected among ground roughnesses (r1 to r6), an elevation, a relative elevation difference, a terrain openness, a wide region terrain openness, aspects (a1 to a7), a slope, a relative slope, a mean elevation, a maximum elevation, a minimum elevation, a relative relief, a distance from a coast, and reinterpretation meteorology data;
a stepwise regression analyzing step (S2) of receiving the output variable and the input variables input in the variable inputting step (S1) and performing a regression analysis method by a stepwise variable selection method; and
a multiple regression equation estimating step (S3) of estimating a multiple regression equation having the highest Coefficient of determination (R2) value using regression coefficients calculated in the stepwise regression analyzing step (S2).
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Abstract
Provided is a method for predicting a wind power density. More particularly, provided are a method for predicting a wind power density using a stepwise regression analysis technique capable of estimating a wind power density at any point using a regression analysis technique by a stepwise variable selection method of performing an analysis while adding statistically important terms or removing statistically meaningless terms.
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Citations
19 Claims
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1. A method for predicting a wind power density using a stepwise regression analysis technique, configured in a form of a program executed by an execution processing means including a computer, comprising:
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a variable inputting step (S1) of inputting the wind power density, which is an output variable, and one or more input variables selected among ground roughnesses (r1 to r6), an elevation, a relative elevation difference, a terrain openness, a wide region terrain openness, aspects (a1 to a7), a slope, a relative slope, a mean elevation, a maximum elevation, a minimum elevation, a relative relief, a distance from a coast, and reinterpretation meteorology data; a stepwise regression analyzing step (S2) of receiving the output variable and the input variables input in the variable inputting step (S1) and performing a regression analysis method by a stepwise variable selection method; and a multiple regression equation estimating step (S3) of estimating a multiple regression equation having the highest Coefficient of determination (R2) value using regression coefficients calculated in the stepwise regression analyzing step (S2). - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for predicting a wind power density using a main component analysis technique, configured in a form of a program executed by an execution processing means including a computer, comprising:
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a variable inputting step (S1) of inputting the wind power density, which is an output variable, dummy variables, which are aspects (a1 to a7) and ground roughnesses (r1 to r6), and one or more input variables selected among an elevation, a relative elevation difference, a terrain openness, a wide region terrain openness, a slope, a relative slope, a mean elevation, a maximum elevation, a minimum elevation, a relative relief, a distance from a coast, and reinterpretation meteorology data; a main component analyzing step (S20) of analyzing the input variables input in the variable inputting step (S10) as a plurality of main components through a main component analysis using eigenvalues and cumulative values; a regression analyzing step (S30) of performing a regression analysis by a stepwise variable selection method using the output variable and the dummy variables input in the variable inputting step (S10) and the input variables analyzed as the plurality of main components in the main component analyzing step (S20); and a multiple regression equation estimating step (S40) of estimating a multiple regression equation having the highest Coefficient of determination (R2) value using regression coefficients calculated in the regression analyzing step (S30). - View Dependent Claims (8, 9, 10, 11, 12)
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13. A method for predicting a wind power density using a neural network analysis technique, configured in a form of a program executed by an execution processing means including a computer, comprising:
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a variable inputting step (S100) of inputting a wind power density, which is an output variable, and one or more input variables selected among ground roughnesses (r1 to r6), an elevation, a relative elevation difference, a terrain openness, a wide region terrain openness, aspects (a1 to a7), a slope, a relative slope, a mean elevation, a maximum elevation, a minimum elevation, a relative relief, a distance from a coast, and reinterpretation meteorology data; a neural network analyzing step (S200) of performing a neural network analysis using the output variable input in the variable inputting step (S100) and the input variables selected through a stepwise variable selection method; and a neural network model estimating step (S300) of estimating a neural network analysis model through a coefficient of correlation value using the number of hidden nodes depending on a root mean square error (RMSE) value calculated in the neural network analyzing step (S200). - View Dependent Claims (14, 15, 16, 17, 18, 19)
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Specification