Research on Power Load Forecasting Based on Random Forest Regression

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Liu, Na^1 ; Hu, Yanzhu^1 ; Ai, Xinbo^1

School of Automation, Beijing University of Posts and Telecommunications, Beijing

100876, China^1

关键词: Denoising methods;    Optimization problems;    Power forecasting;    Power load forecasting;    Prediction accuracy;    Regression algorithms;    Regression predictions;    Trend prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/3/032171/pdf
DOI  :  10.1088/1755-1315/252/3/032171

来源: IOP

【 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.