https://orcid.org/0000-0001-8466-0652
Abstract
When the current algorithm is used to predict the power loss of the low-voltage distribution network, the missing marketing data cannot be processed, which leads to the problem of relatively large root mean square error in the algorithm. To this end, this paper proposes a dynamic prediction algorithm for low-voltage distribution network power loss that combines classification decision trees and marketing data. First, use the classification decision tree to classify the marketing data, and select the missing marketing data. Second, the combined threshold filling method is used to fill the missing data. Finally, the process state characterization method is used to realize the dynamic prediction of the power loss of the low-voltage distribution network based on the complete marketing data. The experimental results show that the data missing ratio of the proposed algorithm is less than 0.2, the root mean square relative error is less than 0.02, and the fitness is higher than 0.08 on average, as with the comparison with the three methods of comparison. The results prove the future prediction to be implemented in a smart city.
Issue Section:
Technical Manuscript
References
1.
C. Q.
Kang
,
Y.
Wang
,
Y. S.
Xue
,
G.
Mu
, and
R. J.
Liao
, “Big Data Analytics in China’s Electric Power Industry: Modern Information, Communication Technologies, and Millions of Smart Meters
,”
IEEE Power and Energy Magazine
16
, no. 3
(April 2018
): 54
–65
,
2.
P.
Dinakara Prasad Reddy
,
V. C.
Veera Reddy
, and
T.
Gowri Manohar
, “Optimal Renewable Resources Placement in Distribution Networks by Combined Power Loss Index and Whale Optimization Algorithms
,”
Journal of Electrical Systems and Information Technology
5
, no. 2
(September 2018
): 175
–191
,
3.
A.
Shefaei
,
M. J.
Vahid-Pakdel
, and
B.
Mohammadi-Ivatloo
, “Application of a Hybrid Evolutionary Algorithm on Reactive Power Compensation Problem of Distribution Network
,”
Computers and Electrical Engineering
72
(November 2018
): 125
–136
,
4.
Z.
Yin
,
J.
Zhang
, and
Y.
Dong
, “Decoupling Analysis of Additional Loss of Key Equipment in Lowvoltage Distribution Network under Compound Power Quality Disturbances
,”
Electrical Measurement and Instrumentation
56
, no. 24
(October 2018
): 59
–66
,
5.
F. Z.
Zhao
,
S.
Hao
,
Y.
Zhang
,
S. H.
Du
,
B. G.
Shan
, and
J.
Su
, “Short-Term Load Forecasting of Distribution Network Based on Variational Modal Decomposition-BA-LSSVM Algorithm
,”
Journal of Agricultural Engineering
35
, no. 14
(September 2019
): 198
–205
,
6.
Z. Y.
Zheng
,
S. D.
Li
,
Y. C.
Ling
,
C.
Cai
, and
Z. J.
Li
, “Calculation and Analysis of Line Loss in Medium and Low Voltage DC Distribution Network
” (in Chinese),
Electrical Measurement and Instrumentation
56
, no. 3
(March 2019
): 31
–36
,
7.
K.
Ramya
,
Y.
Teekaraman
, and
K. A. R.
Kumar
, “Fuzzy-Based Energy Management System with Decision Tree Algorithm for Power Security System
,”
International Journal of Computational Intelligence Systems
12
, no. 2
(February 2019
): 1173
–1178
,
8.
B.
Werners
, “Interactive Multiple Objective Programming Subject to Flexible Constraints
,”
European Journal of Operational Research
31
, no. 3
(September 1987
): 342
–349
,
9.
J. W.
Liang
,
Z.
Qin
,
S.
Xiao
,
L.
Ou
, and
X. D.
Lin
, “Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services
,”
IEEE Transactions on Dependable and Secure Computing
18
, no. 4
(July–August 2021
): 1632
–1644
,
10.
L.
Li
,
S.
Dai
,
Z.
Cao
,
J.
Hong
,
S.
Jiang
, and
K.
Yang
, “Using Improved Gradient-Boosted Decision Tree Algorithm Based on Kalman Filter (GBDT-KF) in Time Series Prediction
,”
The Journal of Supercomputing
76
, no. 9
(September 2020
): 6887
–6900
,
11.
B.-B.
Sang
,
H.-M.
Chen
,
L.
Yang
,
T.-R.
Li
, and
W.-H.
Xu
, “Incremental Feature Selection Using a Conditional Entropy Based on Fuzzy Dominance Neighborhood Rough Sets
,”
IEEE Transactions on Fuzzy Systems
30
, no. 6
(June 2022
): 1683
–1697
,
12.
K. F.
Zheng
,
X. J.
Wang
,
B.
Wu
, and
T.
Wu
, “Feature Subset Selection Combining Maximal Information Entropy and Maximal Information Coefficient
,”
Applied Intelligence
50
, no. 2
(July 2019
): 487
–501
,
13.
J. N.
Wang
and
C.
Su
, “Rapid Recognition of Pavement Damage Based on Decision Tree
,”
Computer Simulation
36
, no. 2
(April 2019
): 427
–430
, 438
,
14.
C.
Su
and
J.
Cao
, “Improving Lazy Decision Tree for Imbalanced Classification by Using Skew-Insensitive Criteria
” (in Chinese),
Applied Intelligence
49
, no. 3
(October 2019
): 1127
–1145
,
15.
H.-R.
Zhang
,
Y.
Yang
,
Y.
Zhang
,
Z.-S.
He
,
W.
Yuan
,
Y.
Yang
,
Q.
Wan
, and
L.
Li
, “A Combined Model Based on SSA, Neural Networks, and LSSVM for Short-Term Electric Load and Price Forecasting
,”
Neural Computing and Applications
33
, no. 2
(July 2020
): 773
–788
,
16.
P.
Vrablecová
,
A. B.
Ezzeddine
,
V.
Rozinajová
,
S.
Šárik
, and
A. K.
Sangaiah
, “Smart Grid Load Forecasting Using Online Support Vector Regression
,”
Computers & Electrical Engineering
65
(January 2018
): 102
–117
,
17.
Y.-T.
Jia
,
L.-M.
Ying
,
D.-H.
Wang
, and
J.
Zhang
, “Defect Prediction of Relay Protection Systems Based on LSSVM-BNDT
,”
IEEE Transactions on Industrial Informatics
17
, no. 1
(January 2021
): 710
–719
,
18.
G.-J.
Wang
,
J.
Lu
,
K.-S.
Choi
, and
G.-Q.
Zhang
, “A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data
,”
IEEE Transactions on Cybernetics
50
, no. 2
(February 2020
): 739
–752
,
19.
J.
Ji
,
C.-S.
Zhang
,
Y.-L.
Gui
,
Q.
Lü
, and
J.
Kodikara
, “New Observations on the Application of LS-SVM in Slope System Reliability Analysis
,”
Journal of Computing in Civil Engineering
31
, no. 2
(March 2017
): 06016002,
20.
A.
Akbarimajd
,
M.
Olyaee
,
B.
Sobhani
, and
H.
Shayeghi
, “Nonlinear Multi-agent Optimal Load Frequency Control Based on Feedback Linearization of Wind Turbines
,”
IEEE Transactions on Sustainable Energy
10
, no. 1
(January 2019
): 66
–74
,
Copyright © 2022
by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959
You do not currently have access to this content.
