Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales
文献类型：期刊论文
| 作者 | Zhang, Tianren1,2; Huang, Yuping1,2,3; Liao, Hui1,3; Gong, Xianfu4; Peng, Bo4 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2024 |
| 卷号 | 12页码:25129-25145 |
| 关键词 | Forecasting Uncertainty Predictive models Wind power generation Analytical models Convolutional neural networks Feature extraction Kernel Density measurement Nonparametric statistics Stability analysis Power grids Wind farm power forecasting (WFPF) uncertainty analysis WOA-CNN-BiLSTM non-parametric kernel density estimation (NPKDE) cloud model (CM) |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2024.3365493 |
| 通讯作者 | Huang, Yuping(huangyp@ms.giec.ac.cn) |
| 英文摘要 | Wind power poses a challenge to the stability of the power grid due to its unpredictability and intermittency. This study aims to analyze the forecasting law and uncertainties of short-term wind farm power forecasting (WFPF) at various time scales, in order to support the stability of energy generation. To achieve this, we propose a framework for short-term WFPF and uncertainty analysis, utilizing the whale optimization algorithm (WOA), convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM), cloud model (CM), and non-parametric kernel density estimation (NPKDE). The data is trained using a hybrid model of CNN-BiLSTM with multiple convolution and pooling methods, while the parameters are optimized using the WOA algorithm. The uncertainty of WFPF is described qualitatively by the expectation, entropy, and hyper-entropy of the cloud model, and quantified through the confidence interval based on non-parametric kernel density estimation. Test results show that the proposed WOA-CNN-BiLSTM model achieves RMSE forecasting errors of 3.79%, 4.52%, and 5.12% at 4 hours, 24 hours, and 72 hours, respectively. The maximum peak errors are less than 10.5758MW, 21.128MW, and 20.0292MW, and are better than other models. Additionally, the WOA optimization performance is superior, consistent with the results described by the cloud model. Furthermore, the RMSE forecasting value of WFPF increases with the time scale, while the growth rate of RMSE decreases with the increase of time scale. This study provides valuable insights into the uncertainties of short-term WFPF and offers a robust framework for improving the stability of energy generation. |
| WOS关键词 | PREDICTION ; MODEL ; ENSEMBLE ; NETWORK ; GENERATION ; SPEED |
| 资助项目 | Guangdong Basic and Applied Basic Research Foundation |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001173075200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Guangdong Basic and Applied Basic Research Foundation |
| 源URL | [http://ir.giec.ac.cn/handle/344007/41008]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Huang, Yuping |
| 作者单位 | 1.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China 2.Univ Sci & Technol China, Sch Energy Sci & Engn, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Key Lab Renewable Energy, Guangzhou 510640, Peoples R China 4.Guangdong Power Grid Co Ltd, Grid Planning & Res Ctr, Guangzhou 510080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tianren,Huang, Yuping,Liao, Hui,et al. Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales[J]. IEEE ACCESS,2024,12:25129-25145. |
| APA | Zhang, Tianren,Huang, Yuping,Liao, Hui,Gong, Xianfu,&Peng, Bo.(2024).Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales.IEEE ACCESS,12,25129-25145. |
| MLA | Zhang, Tianren,et al."Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales".IEEE ACCESS 12(2024):25129-25145. |
入库方式： OAI收割
来源：广州能源研究所
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