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Research and Development of Line Loss Management and Load Forecasting System for Electric Power Enterprises Based on New Energy Consumption Technology Optimization

Jiadong Li

Jiadong Li

State Grid Hebei Marketing Service CenterShijiazhuang, China
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Li, Jiadong
, 
Shifang Hao

Shifang Hao

State Grid Hebei Marketing Service CenterShijiazhuang, China
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Hao, Shifang
, 
Shujun Ji

Shujun Ji

State Grid Hebei Marketing Service CenterShijiazhuang, China
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Ji, Shujun
, 
Kai Liu

Kai Liu

State Grid Hebei Marketing Service CenterShijiazhuang, China
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Liu, Kai
 and 
Linhao Zhang

Linhao Zhang

State Grid Hebei Marketing Service CenterShijiazhuang, China
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Zhang, Linhao
  
Sep 23, 2025
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Published Online: Sep 23, 2025
Received: Jan 17, 2025
Accepted: Apr 20, 2025
DOI: https://doi.org/10.2478/amns-2025-0970
Keywords
© 2025 Jiadong Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

The demand for the surge of the aerial access system
The demand for the surge of the aerial access system

Figure 2.

Consumption obstruction before the increase in the proportion of new energy
Consumption obstruction before the increase in the proportion of new energy

Figure 3.

Consumption obstruction after the increase in the proportion of new energy
Consumption obstruction after the increase in the proportion of new energy

Figure 4.

The structure of LSTNet
The structure of LSTNet

Figure 5.

Federal training information flow based on FedAvg algorithm
Federal training information flow based on FedAvg algorithm

Figure 6.

The overall structure of the industry load prediction based on federal learning
The overall structure of the industry load prediction based on federal learning

Figure 7.

The loss of the global model in different learning rates
The loss of the global model in different learning rates

Figure 8.

User load prediction results under different learning rate
User load prediction results under different learning rate

Figure 9.

Industrial user load forecasting results between global models and local models
Industrial user load forecasting results between global models and local models

Figure 10.

System scheduling structure framework
System scheduling structure framework

Figure 11.

Prediction curve
Prediction curve

Figure 12.

New energy generation power generated by different modes
New energy generation power generated by different modes

Figure 13.

The output of all kinds of power supply
The output of all kinds of power supply

Figure 14.

Backup optimization results
Backup optimization results

Figure 15.

System architecture
System architecture

Figure 16.

Data exchange
Data exchange

Different patterns optimize the scheduling results

Mode Total system cost/$ Backup cost/$ New energy generation capacity/(MW·h)
1 566028.14 286742.25 15267.63
2 572411.81 262593.69 14375.34

Storage parameter

Energy storage capacity/(MW·h) 700
Maximum charge/discharge power of energy storage/MW 120
Upper limit of state of charge 1
Lower limit of state of charge 0.3
Initial state of charge 0.6
Self-discharge rate/% 0.02
Charge/discharge efficiency/% 96

Engine parameters

Unit number Lower limit of output Upper limit of output Cost parameters (a/b/c)/$ / (MW2·h)/$ / (MW·h)/($/h) Start stop cost Minimum start/stop time Climbing power/(MW·h)
1 250 480 0.0464/30/196 2100 9/9 250
2 190 420 0.0510/30/187 1600 8/8 230
3 140 360 0.0118/18.7/162 1200 7/7 160
4 130 320 0.031/13.67/91.3 1150 5/5 110
5 80 160 0.286/16.92/84.5 950 4/4 80

Comparison of model load prediction index

Model The global model The local model
Index MAE MSE RMSE MAE MSE RMSE
Industrial Dataset 0.108 0.043 0.163 0.069 0.022 0.136
Commercial Dataset 0.239 0.084 0.242 0.176 0.041 0.197
Residential Dataset 0.358 0.233 0.469 0.287 0.163 0.407
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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