基于模糊C均值聚类-变分模态分解和群智能优化的多核神经网络短期负荷预测模型

Forecasting Model for Multicore Neural Network Short-term Load Based on Fuzzy C-mean Clustering-variational Modal Decomposition and Chaotic Swarm Intelligence Optimization

电力系统的运行和控制中,短期负荷预测(short-term load forecasting,STLF)起着至关重要的作用。由于负荷的随机性和复杂性,准确预测负荷成为一项挑战。该文将结合了模糊C均值聚类(fuzzy C-means clustering,FCM)理论、变分模态分解(variational modal decomposition,VMD)和混沌粒子群优化(chaotic particle swarm optimization,CPSO)算法的多核极限学习机(multi-kernel extreme learning machine,MKELM)引入到预测模型中,构建聚类、分解、优化、训练、预测的负荷预测模型。然后基于已用于中国南极内陆泰山站能源系统的短期负荷预测应用案例,在原有模型基础上改进后获得适用于中国国内用电负荷预测模型。模型训练结果对比表明,该新模型在负荷短期预测中具有较高精度,能够反映区域用电负荷的变化趋势,研究成果为各种场景的用电负荷预测提供了新方法和新思路。

Short-term load forecasting(STLF)plays a crucial role in the operation and control of power systems.The stochastic nature and complexity of load pose challenges to accurate load forecasting.In this paper,the multi-kernel extreme learning machine(MKELM)optimized by a combination of fuzzy C-means clustering(FCM)theory,variational modal decomposition(VMD)and chaotic particle swarm optimization(CPSO),is introduced into the prediction model,to construct a load forecasting model of clustering,decomposition,optimisation,training,and prediction.Based on the application case of short-term load forecasting for the energy system of Tai Shan Station in the Chinese Antarctic interior,a load forecasting model was improved from the original model,which is applicable to domestic electricity consumption in China is obtained on the basis of the improvements in the original model.A comparison of the model training results shows that the new model has high accuracy in short-term load forecasting and can reflect the trend of regional electricity load,and the research results provide new methods and ideas for electricity load forecasting in various scenarios.