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A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method
| Content Provider | MDPI |
|---|---|
| Author | Dang, Sanlei Peng, Long Zhao, Jingming Li, Jiajie Kong, Zhengmin |
| Copyright Year | 2022 |
| Description | In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts. |
| Starting Page | 663 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15020663 |
| Journal | Energies |
| Issue Number | 2 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-17 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Short-term Load Forecasting Load Point Forecasting Lstm Cnn Quantile Regression Random Forest |
| Content Type | Text |
| Resource Type | Article |
