电力系统自动化2019，Vol.43Issue(1)：65-74,10.DOI:10.7500/AEPS20180625009
基于门控循环单元网络与模型融合的负荷聚合体预测方法
Aggregated Load Forecasting Method Based on Gated Recurrent Unit Networks and Model Fusion
摘要
Abstract
With the popularity of smart meters, the aggregated load can be flexibly divided into different sizes according to different requirements and be predicted based on the measurement data.Due to the large difference in the scales of aggregated loads and the close relationship with the load characteristics of users, the traditional prediction method is no longer applicable.This paper proposes an aggregated load forecasting method based on gated recurrent unit (GRU) networks and model fusion.Firstly, load groups with similar load characteristics are clustered by the distributed spectral clustering algorithm, then grouping predictions are employed.Secondly, GRU is adopted as a meta-model to perform dynamic modeling of time series, and several different structures of GRU networks are fused by random forest algorithm to realize the load group forecast.Finally, the aggregated load forecast value can be obtained by summing prediction value of each group.Benefiting from the grouping prediction, dynamic time modeling and model fusion technology, the proposed method can make full use of advantages of different model structures and discover the dynamic rule of time series.The proposed method achieves higher prediction accuracy and higher adaptability for aggregated load with different scales.关键词
负荷预测/谱聚类/门控循环单元/模型融合Key words
load forecasting/spectral clustering/gated recurrent unit (GRU)/model fusion引用本文复制引用
陈海文,王守相,王绍敏,王丹..基于门控循环单元网络与模型融合的负荷聚合体预测方法[J].电力系统自动化,2019,43(1):65-74,10.基金项目
国家重点研发计划资助项目(2018YFB0905000) This work is supported by National Key R&D Program of China (No. 2018YFB0905000) （2018YFB0905000）
