Clustering algorithm of power load curves in distribution network based on analysis of matrix characteristic roots

2016 ◽  
Author(s):  
Yuqi Ji ◽  
Dongli Jia ◽  
Keyan Liu ◽  
Kaiyuan He ◽  
Lingzhi Lu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Distribution Network ◽  
Characteristic Roots ◽  
Power Load ◽  
Matrix Characteristic

scholarly journals Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

2019 ◽  
Vol 11 (24) ◽  
pp. 6954
Author(s):  
Fuqiang Li ◽  
Shiying Zhang ◽  
Wenxuan Li ◽  
Wei Zhao ◽  
Bingkang Li ◽  
...  
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Kernel Density ◽  
Support Vector ◽  
Average Width ◽  
Power Load ◽  
Density Forecasting

In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

scholarly journals A Customer Clustering Algorithm for Power Logistics Distribution Network Structure and Distribution Volume Constraints

2021 ◽  
Vol 15 ◽  
pp. 1051-1056
Author(s):  
Jianying Zhong ◽  
Jibin Zhu ◽  
Yonghao Guo ◽  
Yunxin Chang ◽  
Chaofeng Zhu
Keyword(s):  
Network Structure ◽  
Clustering Algorithm ◽  
Distribution Network ◽  
Distribution Volume ◽  
Distribution Center ◽  
Vehicle Scheduling ◽  
Route Optimization ◽  
Scheduling Optimization ◽  
Logistics Distribution ◽  
Center Location

Customer clustering technology for distribution process is widely used in location selection, distribution route optimization and vehicle scheduling optimization of power logistics distribution center. Aiming at the problem of customer clustering with unknown distribution center location, this paper proposes a clustering algorithm considering distribution network structure and distribution volume constraint, which makes up for the defect that the classical Euclidean distance does not consider the distribution road information. This paper proposes a logistics distribution customer clustering algorithm, which improves CLARANS algorithm to make the clustering results meet the constraints of customer distribution volume. By using the single vehicle load rate, the sufficient conditions for logistics distribution customer clustering to be solvable under the condition of considering the ubiquitous and constraints are given, which effectively solves the problem of logistics distribution customer clustering with sum constraints. The results state clearly that the clustering algorithm can effectively deal with large-scale spatial data sets, and the clustering process is not affected by isolated customers, The clustering results can be effectively applied to the distribution center location, distribution cost optimization, distribution route optimization and distribution area division of vehicle scheduling optimization.

Power load clustering algorithm for demand response

2019 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Yanguang Cai ◽  
Helie Huang ◽  
Hao Cai ◽  
Yuanhang Qi
Keyword(s):  
Demand Response ◽  
Clustering Algorithm ◽  
Power Load

Research on Reclassification of Power Users for TOU Power Price Based on Fuzzy Clustering

2012 ◽  
Vol 229-231 ◽  
pp. 1013-1016
Author(s):  
Ling Luo ◽  
Bao Chen Jiang ◽  
Li Kai Liang
Keyword(s):  
Power Consumption ◽  
Fuzzy Clustering ◽  
Theoretical Basis ◽  
Clustering Algorithm ◽  
Simulation Analysis ◽  
Reference Value ◽  
Actual Condition ◽  
Load Curve ◽  
Fuzzy Clustering Algorithm ◽  
Power Load

Study on TOU (Time-of-Use) power price in our country almost treats power users as a unified whole, but different categories of users have the different power consumption and way in the actual condition. Therefore, they have different responses to TOU power price. According to the power load features of all kinds of users, the paper presents a solution to reclassify the users using fuzzy clustering algorithm, and provides theoretical basis for implementing categorized TOU power price. Finally comparatively perfect effect is obtained by simulation analysis, and it has great reference value to perfect TOU power price and improve load curve.

The Influence on Clustering Results of Electricity Load Curves Using Different Distances

2013 ◽  
Vol 401-403 ◽  
pp. 1440-1443 ◽  
Author(s):  
Tie Feng Zhang ◽  
Fei Lv ◽  
Rong Gu
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Pattern Extraction ◽  
Clustering Techniques ◽  
Comparison Results ◽  
Power Load ◽  
Load Pattern ◽  
Electricity Load ◽  
Mining Algorithms

Distance choice is an important issue in power load pattern extraction using clustering techniques, so it is necessary to find the influence on clustering result of load curves using different distances in clustering algorithms. In this paper several distances are used in the k-means algorithm for clustering load curves and their influences on the clustering results are analyzed, therefore, the suitable distance for the k-means algorithms is obtained. An example with 147 electricity customers load curves shows distances have different influences on clustering results using the same clustering algorithm. The comparison results indicate that the choice of distances is an important issue in power load pattern extraction using clustering techniques and a suitable distance may improve the accuracy of mining algorithms.

scholarly journals B2C E-COMMERCE CUSTOMIZED LOGISTICS AREA INVENTORY ALLOCATION STRATEGY BASED ON CONSTRAINED CLUSTERING ALGORITHM

2018 ◽  
Vol 48 (3) ◽  
pp. 217-222
Author(s):  
J. K. HUANG
Keyword(s):  
Growth Rate ◽  
Clustering Algorithm ◽  
Online Shopping ◽  
Distribution Network ◽  
Distribution Area ◽  
Logistics Network ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Inventory Allocation ◽  
At Home

Logistics network includes regional logistics network and urban logistics network. In this paper, the urban logistics network is taken as the research object. With the improvement of the consumption level of the residents, the attention to quality has become an important reference standard for consumers to choose e-commerce, which makes the electric business shift from "price war" to "service war". Compared with past purchases in physical stores, most consumers prefer to choose convenient and fast online shopping. As a result, the size of the online shopping market has increased rapidly. According to statistics, the growth rate of the online shopping market in the past five years is over 100%, and the growth rate will slow down in the next few years, but it will still maintain steady growth. The importance of logistics for an e-business enterprise is obvious. The improvement and perfection of logistics distribution network is imminent. Scholars at home and abroad have studied this aspect for a long time. This research is based on the optimization of ecommerce logistics distribution network. By summing up the ideas and solutions proposed by researchers at home and abroad for this problem, and combining with the actual situation, a method of optimizing the B2C e-commerce logistics distribution network is designed. Considering the special traffic situation and the actual order demand in the city, the distribution area division, the distribution site stratification, the vehicle routing optimization and the logistics network optimization model are set up, and a combination of various methods is used to solve the problem.

Sign in / Sign up

Export Citation Format

Share Document