원문정보
초록
영어
The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back Propagation (BP) neural network algorithm and genetic algorithm (GA) are combined to establish the prediction model of the short-term wind farm output power based on meteorological data collected by Wireless Sensor Network (WSN). The Meteorological data is used to determine the input variables of the BP neural network. Meanwhile, the GA is used to adjust the value of BP's connection weight and threshold dynamically. Then the trained BP neural network is used to predict the wind power. The experiment results show that our method has better prediction capability compared with that using BP neural network alone or using wind power formulas.
목차
1. Introduction
2. Meteorological Data Collected by WSN
3. Data and Methods
3.1 Data and the Number of Neural Network Input Variables Determination
3.2 The Connection Weights and Thresholds of BP Neural Network Adjusted by GA
4 Experiment Analysis
5. Conclusions
References