基于模态组合的短期负荷预测方法

随着电力市场化改革的深入推进,电力系统运行呈现出更强的灵活性和不确定性,对短期负荷精准预测提出了更高的要求。为有效协调发电、输电、配电、用电的关系,增强电力系统日运行调度的安全稳定性,该研究提出了一种基于模态组合的短期负荷预测方法。从时域和频域2个维度提出了负荷序列和影响因素序列分解评价方法,得到改进的变分模态分解法(Variational Mode Decomposition,VMD),对原始日负荷序列及影响因素序列进行分解,并采用粒子群优化算法(Particle Swarm Optimization,PSO)对VMD的参数寻优。根据原始序列分解情况,对比各影响因素模态序列与负荷序列的频域特征,筛选出与负荷序列特征相近的模态并将其进行线性叠加组合,得到多个整合后的组合模态序列,以确定预测模型的输入量;分别利用粒子群优化的最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)模型进行负荷预测。算例结果表明,相比于直接采用PSO-LSSVM方法,本文提出的基于模态组合的短期负荷预测方法的最大相对误差降低了3.36个百分点,平均相对误差降低了1.71个百分点,最大绝对误差降低了95 MW,平均绝对误差降低了55.72 MW,短期负荷预测的精度得到明显提升。

Short term load prediction method based on modal combination

Abstract: Short-term load forecasting plays a central role in the daily operation and dispatching of power systems. Greater flexibility and uncertainty in the operation of power systems have brought harsh requirements on the accurate forecasting of short-term load, particularly with the in-depth advancement of power market reform. A high-precision prediction model is also highly demanding to effectively coordinate the relationship between power generation, transmission, distribution, and consumption. In this study, short-term load forecasting was therefore proposed using variational mode decomposition (VMD) and particle swarm optimization (PSO) modal combination. The VMD was adopted for the adaptive signal decomposition of load sequence, considering the time and frequency domain in the signal decomposition evaluation. The impact of two dimensions on authenticity, independence, and performance was quantitatively clarified, further to determine the evaluation indicators of signal decomposition. An authenticity test of signal decomposition included the redundant component and residual difference component. In redundant components, the Pearson correlation coefficient of the component and the original signal was compared with the authenticity threshold from the perspective of the time domain. In residual components, the ratio of frequency band with significant amplitude in the residual spectrum to the original signal frequency band was used to measure the spectral characteristics of residual components from the perspective of the frequency domain. In the independence index test, two indicators were used to evaluate in the two dimensions of time and frequency domain, including the average Pearson correlation coefficient between the signal components, and the average overlap of significant frequency bands. Time domain was measured using the average Pearson correlation coefficient of each component and the original signal. The average concentration index of the significant frequency band was also selected to verify the signal decomposition. Three indicators were integrated to establish a comprehensive evaluation indicator for signal decomposition. PSO was then used to optimize the parameters of the VMD model, where the comprehensive evaluation index of signal decomposition was taken as the objective function. The influencing factors of daily load included temperature, humidity, historical load, and day-ahead electricity price. The modal combination was used to integrate the modal sequence of influencing factors with similar periodicity in the frequency domain of load. The number of influencing factors after combination was expanded the same as the significant frequency bands of the load frequency domain, where the combined influencing factor modal presented a strong correlation with the load sequence. The expanded variables of influencing factors were input into the PSO-least squares support vector machine (LSSVM) model for the load forecast of the day. The simulation results show that the VMD decomposition using the evaluation index optimization was better than the wavelet analysis, optimized ensemble empirical mode decomposition (EEMD), and non-optimized VMD, indicating the decomposed modal sequence behaved higher quality. Specifically, The maximum relative error increased by 3.36%, the average relative error increased by 1.71%, the maximum absolute error increased by 95MW, and the average absolute error increased by 55.72MW, compared with the PSO-LSSVM using a modal combination. This finding can provide sound support to the construction of a power market with flexible power regulation under the penetration of a high proportion of renewable energy, efficient grid interconnection, and extensive user response.