精准可靠的冷、热、电负荷预测对综合能源系统的优化运行具有重要意义,为有效提取负荷序列间存在的线性、非线性、耦合性以及不确定性等特征,该文提出一种由多元线性回归（MLR）、改进型自适应白噪声完备集成经验模态分解（ICEEMDAN）、长短时记忆（LSTM）神经网络、蒙特卡罗（MC）法相结合的多元负荷预测方法。首先,针对冷、热、电负荷分别构建MLR模型以挖掘线性特征。然后,将残差部分利用ICEEMDAN方法分解,再对重构后同一频段的各负荷残差分量建立LSTM模型,实现对非线性及耦合性的学习。最后,将MLR与LSTM结果叠加得到点预测值。与参照模型中的最优结果相比,该方法下冷、热、电负荷的R2分别提升了0.09%、0.21%、0.40%。此外,为实现对负荷不确定性的有效量化,进一步采用非参数核密度估计与MC抽样结合的方法得到预测区间结果。经算例分析,各负荷的预测区间覆盖率均大于相应的置信水平（95%、90%、85%）,所提方法具有较高的预测精度及可靠性。
Abstract
Accurate and reliable cold, heat and electrical load forecasting is a prerequisite for optimal scheduling and efficient operation of integrated energy system. To effectively extract the linear, nonlinear, coupling and uncertainty characteristics existing among load sequences, this paper proposes a multiple linear regression （MLR）, improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）, long short-time memory （LSTM） neural network, and Monte Carlo （MC） method combined with multivariate load prediction method. First, MLR models are constructed separately for cold, heat and electrical load to mine the linear features. Then, the residual components are decomposed using the ICEEMDAN method, and LSTM models are built for each load residual component in the same frequency band after the component reconstruction to realize the learning of nonlinearity and coupling. Finally, the point prediction values are acquired by summing the MLR and LSTM results. Compared with the optimal results in the reference model, the R2 of the method improves 0.09%,0.21%,and 0.40% for cold,heat,and electrical load, respectively. In addition, to achieve an effective quantification of load uncertainty, a combination of nonparametric kernel density estimation and MC sampling is further used to obtain the prediction interval results. After the example analysis, the prediction interval coverage probability of each load is greater than the corresponding confidence level （95%,90%,85%）, and the proposed method has high prediction accuracy and reliability.
关键词
负荷预测 /
综合能源系统 /
模态分解 /
长短时记忆神经网络 /
蒙特卡罗
Key words
load forecasting /
integrated energy system /
mode decomposition /
long short-time memory neural network /
Monte Carlo
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基金
国家电网有限公司总部科技项目（5100-202199531A-0-5-ZN）
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