| 摘要: |
| 准确的风速预测能够促进大规模的风电并网，保证电力系统的安全稳定运行。针对传统点预测方法难以表征预测结果概率可信度问题，提出一种基于模糊信息粒化、改进长短期记忆网络与差分自回归移动平均模型的混合区间预测模型。首先，采用自适应噪声的完全集合经验模态分解模型对原始风速数据进行分解，并依据模糊熵重构得到新序列。在此基础上，对每个序列依次进行模糊信息粒化，获得最大值、最小值及平均值。最后，利用改进长短期记忆网络模型预测高频序列，差分自回归移动平均模型预测低频序列与余项，并将所得上下界求和得到最终风速区间。算例分析表明，所提模型得出的风速预测区间能够准确覆盖实测风速，为电力系统调度提供更多有价值的决策信息。 |
| 关键词: 风速区间预测 模糊信息粒化 改进长短期记忆神经网络 差分自回归移动平均模型 混合模型 |
| DOI：DOI:?10.19783/j.cnki.pspc.220241 |
| 投稿时间：2022-02-28修订日期：2022-05-23 |
| 基金项目:国家自然科学基金项目资助(71774054)；中央高校基本科研业务专项资金资助(2019MS055) |
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| Ultra short term wind speed interval prediction based on a hybrid model |
| ZHANG Jinliang,LIU Ziyi |
| (School of Economics and Management, North China Electric Power University, Beijing 102206, China) |
| Abstract: |
| Accurate wind speed prediction can promote large-scale wind power integration and ensure the safe and stable operation of a power system. There is a problem in that traditional point prediction methods find it difficult to represent the probability credibility of prediction results. This paper proposes a hybrid interval prediction model based on fuzzy information granulation, an improved long short-term memory network and an autoregressive integrated moving average model. First, the original wind speed data is decomposed by a complete set empirical mode decomposition model of adaptive noise, and the new sequence is reconstructed according to fuzzy entropy. Then the fuzzy information of each sequence is granulated to obtain the maximum, minimum and average values. Finally, the improved long short-term memory network model is used to predict the high-frequency series, and the autoregressive integrated moving average model is used to predict the low-frequency series and the remainder, and then the obtained upper and lower bounds are summed to obtain the final wind speed interval. Example analysis shows that the wind speed prediction interval obtained by this model can accurately cover the measured wind speed and provide more valuable decision-making information for power system dispatching.
This work is supported by the National Natural Science Foundation of China (No. 71774054). |
| Key words: wind speed interval prediction fuzzy information granulation improved long short-term memory neural network ARIMA model hybrid model |
