| 引用本文: | 杨斌,杨世海,曹晓冬,等.基于EMD-QRF的用户负荷概率密度预测[J].电力系统保护与控制,2019,47(16):1-7.[点击复制] |
| YANG Bin,YANG Shihai,CAO Xiaodong,et al.Short-term consumer load probability density forecasting based on EMD-QRF[J].Power System Protection and Control,2019,47(16):1-7[点击复制] |
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| 摘要: |
| 考虑用户负荷时间序列基数小、波动性与随机性强、难以取得较高预测精度的特点，建立基于经验模式分解(Empirical Mode Decomposition, EMD)与分位数回归森林(Quantile Regression Forest, QRF)的用户负荷概率密度组合预测模型，以提高用户负荷预测精度。首先，采用EMD信号处理算法对用户负荷原始时间序列数据进行分解处理，计算各模态函数样本熵值并根据样本熵大小对模态函数进行重构。其次，对重构分量分别建立QRF用户负荷预测模型，叠加不同分量预测结果从而获得预测值的条件分布。最后，利用核密度估计输出任意时刻用户负荷概率密度预测结果。相对于确定性点预测方法，概率密度预测具有描述用户负荷未来可能的波动范围及不确定性等优势，且算例测试验证了模型的有效性。 |
| 关键词: 用户负荷 经验模式分解 分位数回归森林 核密度估计 概率密度预测 |
| DOI：10.19783/j.cnki.pspc.181207 |
| 投稿时间：2018-09-25修订日期：2018-11-27 |
| 基金项目:国家自然科学基金项目资助(51507052) |
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| Short-term consumer load probability density forecasting based on EMD-QRF |
| YANG Bin,YANG Shihai,CAO Xiaodong,CHEN Yuqin,LIANG Zhi,SUN Guoqiang |
| (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China;State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China) |
| Abstract: |
| Considering the small base of consumer load time series, strong volatility and randomness, along with the difficulty of obtaining high forecasting accuracy, a hybrid model based on Empirical Mode Decomposition (EMD) and Quantile Regression Forest (QRF) is proposed for consumer load probability density forecasting, which is aimed at enhancing prediction precision. Firstly, the signal processing algorithm of EMD is applied to decompose the original consumer load time series, where the sample entropy of each decomposed mode function is calculated. Based on the values of sample entropy, the mode functions can be reconstructed. Then, each reconstructed component is modeled separately using QRF for consumer load forecasting, where the conditional distribution of predicted values can be obtained by superimposing prediction results of different components. Finally, the Kernel Density Estimation (KDE) is used to output the consumer load probability density forecasting results at any time. Compared with deterministic point prediction methods, the proposed probability density forecasting model has advantages of describing the possible fluctuation range and uncertainty of the consumer load in the future, where the case study has also verified its validity. This work is supported by National Natural Science Foundation of China (No. 51507052). |
| Key words: consumer load empirical mode decomposition quantile regression forest kernel density estimation probability density forecasting |
