外推法
多元统计
期限(时间)
集合(抽象数据类型)
聚类分析
联轴节(管道)
计算机科学
相似性(几何)
系列(地层学)
多元分析
数据挖掘
算法
数学
统计
人工智能
工程类
机器学习
物理
图像(数学)
古生物学
生物
机械工程
量子力学
程序设计语言
作者
Ming Yang,Dongxu Li,Xin Su,Jinxin Wang,Yu Cui
标识
DOI:10.3389/fenrg.2022.1037874
摘要
Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement, which is tested by the fluctuation of load sequences and the accuracy of Numerical Weather Prediction (NWP). This paper proposes a multiple load prediction method considering the coupling characteristics of multiple loads and the division of load similar fluctuation sets. Firstly, the coupling characteristics of multivariate loads are studied to explore the interaction relationship between multivariate loads and find out the priority of multivariate load prediction. Secondly, the similar fluctuating sets of loads are divided considering the similarity and fluctuation of load sequences. Thirdly, the load scenarios are divided by k-means clustering for the inter-set sequences of similar fluctuating sets, and the Bi-directional Long Short-Term Memory (BI-LSTM) models are trained separately for the sub-set of scenarios and prioritized by prediction. Finally, the effectiveness of the proposed method was verified by combining the multivariate load data provided by the Campus Metabolism system of Arizona State University.
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