Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification
| Autor: | Xing Li, Pei Zhang, Yanling Wang, Qingtian Duan, Likai Liang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
DBSCAN
Computer science 020209 energy GA-BP neural network Bioengineering 02 engineering and technology lcsh:Chemical technology computer.software_genre Wind speed lcsh:Chemistry DBSCAN algorithm 020204 information systems Genetic algorithm 0202 electrical engineering electronic engineering information engineering Chemical Engineering (miscellaneous) lcsh:TP1-1185 Wind power Artificial neural network business.industry Process Chemistry and Technology short-term wind power prediction Power (physics) Euclidean distance lcsh:QD1-999 outlier identification Outlier Data mining business computer linear regression method |
| Zdroj: | Processes Volume 8 Issue 2 Processes, Vol 8, Iss 2, p 157 (2020) |
| ISSN: | 2227-9717 |
| DOI: | 10.3390/pr8020157 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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