Wind Power Density Prediction Using Stepwise Regression
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Solution Overview
Problem
Current methods for predicting wind power density require significant computational resources and time, making them costly and inefficient, especially when using microscopic or middle-scale air flow models to analyze wind power resources.
Innovation Solution
A method utilizing stepwise regression analysis, main component analysis, and neural network techniques to predict wind power density by selecting statistically significant variables and transforming data, reducing the need for extensive computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microscopic or middle-scale air flow models are used to analyze wind power resources, then measurement precision is improved, but productivity deteriorates due to significant computational resources and time requirements
Solution Approach 1:
The patent replaces expensive, computationally intensive air flow models with a simpler regression-based prediction model that uses pre-processed terrain and meteorological data. This disposable-like approach creates a lightweight model that can be quickly executed without requiring significant computational resources, thereby resolving the contradiction between precision and productivity.
Solution Approach 2:
The patent performs preliminary processing of terrain data (elevation, slope, aspect) and meteorological data (wind speed, temperature, pressure) before the actual wind power density calculation. By pre-processing and selecting only the most relevant variables through stepwise regression, the model reduces computational complexity while maintaining prediction accuracy, thus improving productivity without sacrificing measurement precision.
2Measurement precision
If comprehensive terrain and meteorological data are collected for wind power analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the most statistically significant variables from the comprehensive dataset using stepwise regression analysis. Instead of processing all available terrain and meteorological data, the model selectively extracts key predictors (such as elevation, wind speed, and temperature) that have the strongest correlation with wind power density, thereby reducing system complexity while maintaining measurement precision.
Solution Approach 2:
The patent transforms raw terrain and meteorological parameters into standardized variables through normalization and selects optimal parameter combinations based on statistical significance. By changing the form and selection of parameters rather than processing all original data, the system reduces complexity while preserving the essential information needed for accurate wind power density prediction.
Data Source
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AI summary
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, as a method of selecting variables that are to be used in a multiple regression analysis using a linear relationship between variables belonging to a data set, a method for predicting a wind power density using a main component analysis technique of providing a linear regression analysis model capable of estimating a linear relationship with a wind power density, which is an output variable, by classifying input variables into a plurality of main components configured of linear combinations using a variance/covariance relationship and using the classified main component input variables as new input variables between which multicollinearity is not present, and a method for predicting a wind power density using a neural network analysis technique of predicting a wind power density at any point by modeling a non-linear relationship between variables having a complicated structure through the neural network analysis technique of using variables selected by a stepwise variable selection method as input variables and using values obtained by transforming a wind power density as output variables.