基于自适应噪声完备经验模态分解−样本熵−长短期记忆神经网络和核密度估计的短期电力负荷区间预测
摘要：短期电力负荷具有较强的随机性和波动性，其预测的准确性对于提升供电可靠性、电力系统运行经济性至关重要。针对传统确定性预测不能反映未来负荷波动的弊端，基于“点预测+区间估计”的思路提出了一种短期负荷区间预测方法。首先基于自适应噪声完备经验模态分解方法将负荷序列分解为多个模态分量，并根据不同序列样本熵的计算结果将序列进行重构以降低运算量。在此基础上，针对每一个分量分别构建长短期记忆神经网络预测模型，得到未来负荷点预测值。基于此利用核密度估计方法对预测误差的分布进行估计，进而结合点预测结果实现未来短期负荷的区间预测。通过将此模型与其他模型进行对比，结果表明此模型能够实现更低的点预测误差，同时在区间预测中也表现出更好的综合性能。
Abstract:Short-term power load possesses strong randomness and volatility, and the accuracy of its prediction is of importance to raise both power supply reliability and power system operation economy. In allusion to the disadvantage that traditional deterministic prediction cannot reflect future load fluctuation, based on the idea of "point forecast plus interval estimation" a short-term load interval prediction method was proposed. Firstly, based on complete ensemble empirical mode decomposition with adaptive noise method (abbr. CEEMDAN), the load series was decomposed into multi modal components, and according to the calculation results of sample entropy (abbr. SE) of different sequences the sequences were reconstructed to reduce the computation. Secondly, on this basis, a long and short-term memory (abbr. LSTM) neural network prediction model was constructed respectively for each component to obtain the predicted value of the future load point. Finally, based on above-mentioned work, the kernel density estimation (abbr. KDE) method was utilized to estimate the distribution of the prediction error, further, combining with point prediction results the interval prediction of future short-term load was implemented. Comparing the proposed model with other models, the results show that using the proposed model a lower point prediction error can be implemented, meanwhile, the proposed model exerts a better combination property in the interval prediction.
标题：基于自适应噪声完备经验模态分解−样本熵−长短期记忆神经网络和核密度估计的短期电力负荷区间预测
英文标题：Short-term Power Load Interval Prediction Based on CEEMDAN-SE-LSTM and KDE
作者：赵会茹, 张士营, 赵一航, 刘红雨, 邱宝红,
关键词：短期负荷预测, 自适应噪声完备经验模态分解, 长短期记忆神经网络, 核密度估计,
发表日期：2021-04-10
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