Abstract
Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.
Highlights
Wind is pollution-free, abundant, and widely distributed—which is one of the most important energy sources for generating electricity
In Reference [3], a prediction model using reverse back propagation-artificial neural network (BP-ANN) is established, which is 10 min and 1 h ahead of time, and an improved model is established by system error revision and wake coefficient improvement
For most wind power prediction, only simple pre-processing is adopted for the outliers in power data, such as using deviation rate or pauta criterion to identify the outliers, and using mean value, mode or hotdecking method to correct the outliers. Using these methods will cause the temporality of wind speed and power being ignored, which will lose the characteristic of the data and decrease the accuracy of the prediction model
Summary
Wind is pollution-free, abundant, and widely distributed—which is one of the most important energy sources for generating electricity. In Reference [4], a prediction model using back propagation (BP) neural network, which is combined with wavelet, is established based on weather forecast data. For most wind power prediction, only simple pre-processing is adopted for the outliers in power data, such as using deviation rate or pauta criterion to identify the outliers, and using mean value, mode or hotdecking method to correct the outliers. Using these methods will cause the temporality of wind speed and power being ignored, which will lose the characteristic of the data and decrease the accuracy of the prediction model. The error analysis proves the feasibility and effectiveness of the prediction model
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