期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：3
页码：1-9
DOI：10.1088/1755-1315/252/3/032171
出版社：IOP Publishing
摘要：In recent years, with the development of artificial intelligence, power forecasting based on big data analysis has gradually become intelligent. In order to improve the prediction accuracy and efficiency of the model in dealing with large volume data, this paper combines the compressed sensing algorithm and the random forest regression algorithm. The discrete cosine transform base is used to sparsely represent the data. The original data is restored by solving the norm optimization problem to achieve the purpose of denoising. And the processed data is used for regression prediction, it achieves great results. The results indicate that the compressed sensing algorithm can retain more details and get better effect compared with the traditional denoising method. Combined with the random forest regression algorithm, the prediction accuracy of the model is improved. This method can be implemented in the trend prediction of time series data such as power load, which has very important practical significance.
