摘要
负荷预测自电力工业诞生便是一项热门的基础研究问题,连续多天的日高峰负荷预测往往对电网的优化运行与安全稳定起到重要作用。该文深入分析了统计学习类算法的误差分布,提出一种基于串–并行集成学习的连续多日高峰负荷预测方法。首先介绍了统计学习类算法的泛化误差的分解情况,阐述了XGBoost串行集成算法与Bagging并行集成学习的训练机理,分析了粒子群算法的基本原理。在综合考量模型偏差与方差分布的基础上,提出Bagging框架下基于XGBoost算法的负荷预测模型,并采用粒子群算法交叉验证XGBoost模型最优超参数。最后,使用斯洛文尼亚电力公司用电负荷数据对算法有效性进行验证,算例表明XGBoost模型对特征贡献度的量化分析有效地辅助了特征选择的过程,粒子群算法缩短了XGBoost超参数寻优的时间。与传统模型相比,基学习器为树模型的Bagging-XGBoost算法有着较高的预测精度。预测结果显示串–并行方式耦合的集成学习方式在连续多日高峰负荷预测场景中有着较高应用价值。
Load forecasting is a hot research since the birth of the electric power industry. Daily peak load forecasting for consecutive days often plays an important role in the operation, security and stability of power grid. In this paper, consecutive daily peak load model based on sequential-parallel ensemble learning was proposed considering the forecasting error distribution of statistical learning algorithms. Firstly, the decoupling process of generalization error was analyzed, the mechanism of XGBoost sequential ensemble learning, bagging parallel ensemble learning and particle swarm optimization were introduced. Then, the load forecasting based on XGBoost algorithm under bagging framework were deployed, associated with the distribution of bias and variance at the training stage. The PSO was used to cross validate the hyperparameters of XGBoost model. Finally, the effectiveness of the algorithm was verified by Slovenia power load data. The results show that XGBoost algorithm calculates the characteristic importance to aid to select the valuable features. The particle swarm optimization algorithm effectively shorten duration of hyperparameters optimization. And the BaggingXGBoost algorithm has better forecasting accuracy compared with several conventional models. The forecasting results indicate that sequential-parallel ensemble learning method have higher application value in engineering application for load forecasting.
作者
史佳琪
马丽雅
李晨晨
刘念
张建华
SHI Jiaqi;MA Liya;LI Chenchen;LIU Nian;ZHANG Jianhua(State Key Laboratory of Alternative Electrical Power System With Renewable Energy Sources(North China Electric Power University),Changping District,Beijing 102206,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2020年第14期4463-4472,共10页
Proceedings of the CSEE
基金
国家电网公司总部科技项目(SGJX0000KXJS1900321)。
