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A neural network model-based approach for power data collection and load forecasting accuracy improvement

Yiran Li

Yiran Li

State Grid Yingda Chang’an Insurance Brokerage Co., LtdChina
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Li, Yiran
, 
Yanning Xue

Yanning Xue

State Grid Yingda Chang’an Insurance Brokerage Co., LtdChina
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Xue, Yanning
, 
Zhongli Wang

Zhongli Wang

State Grid Yingda Chang’an Insurance Brokerage Co., LtdChina
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Wang, Zhongli
 e 
Linxua Guo

Linxua Guo

State Grid Yingda Chang’an Insurance Brokerage Co., LtdChina
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Guo, Linxua
  
23 set 2025
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Pubblicato online: 23 set 2025
Ricevuto: 17 gen 2025
Accettato: 28 apr 2025
DOI: https://doi.org/10.2478/amns-2025-1107
Parole chiave
© 2025 Yiran Li, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

The daily periodic curve of the power load
The daily periodic curve of the power load

Figure 2.

Continuous peripheral load curve
Continuous peripheral load curve

Figure 3.

The load curve of the five holidays
The load curve of the five holidays

Figure 4.

Load curve
Load curve

Figure 5.

Four seasons of a working day load curve
Four seasons of a working day load curve

Figure 6.

Summer daily load and temperature change curve
Summer daily load and temperature change curve

Figure 7.

Preprocessing flowchart
Preprocessing flowchart

Figure 8.

Topology comparison of RNN and LSTM
Topology comparison of RNN and LSTM

Figure 9.

LSTM neural network structure
LSTM neural network structure

Figure 10.

Correspondence diagram
Correspondence diagram

Figure 11.

The PSO-LSTM model predicts the future of the day
The PSO-LSTM model predicts the future of the day

Figure 12.

The PSO-LSTM model predicts the outcome of the next week
The PSO-LSTM model predicts the outcome of the next week

Figure 13.

The PSO-LSTM model predicts a day of comparison
The PSO-LSTM model predicts a day of comparison

Figure 14.

The PSO-LSTM model predicts a day of comparison
The PSO-LSTM model predicts a day of comparison

Different model loss values

Iteration number RNN LSTM PSO-LSTM Iteration number RNN LSTM PSO-LSTM
1 78.67527 43.2962 42.36194 1601 0.89698 0.47853 0.41502
101 42.44801 10.79734 7.30219 1701 0.89135 0.82218 0.54316
201 15.22501 5.05437 2.3731 1801 0.60634 0.57307 0.43421
301 6.24314 2.21514 1.60676 1901 0.74373 0.66735 0.52725
401 8.16502 1.19442 1.21793 2001 0.96443 0.77352 0.58641
501 4.87227 1.59874 1.02371 2101 0.526 0.50255 0.38669
601 1.63241 0.862 0.58116 2201 0.71479 0.67203 0.43595
701 1.27819 0.7606 0.69419 2301 0.5096 0.43901 0.39459
801 1.30943 0.50601 0.42087 2401 0.6232 0.46931 0.23918
901 0.8181 1.01908 0.59515 2501 0.62649 0.58077 0.41896
1001 0.73823 0.60342 0.33513 2601 0.73184 0.66491 0.33267
1101 0.65579 0.57555 0.46369 2701 0.49047 0.20363 0.22886
1201 0.72981 0.58097 0.55145 2801 0.40685 0.34304 0.26968
1301 0.90479 0.5165 0.35502 2901 0.4446 0.27034 0.23535
1401 0.63814 0.29595 0.35681 3000 0.41062 0.23253 0.24206
1501 0.71033 0.68427 0.50759 3100 0.34905 0.20404 0.1962

PSO-LSTM prediction results

Time Primordial PSO-LSTM Time Primordial PSO-LSTM
1 0.18651 0.16131 51 0.18943 0.21547
2 0.11709 0.14586 52 0.14074 0.196
3 0.10167 0.11327 53 0.23454 0.21433
4 0.09451 0.12609 54 0.31057 0.26182
5 0.1735 0.16595 55 0.39798 0.34969
6 0.22614 0.23707 56 0.51756 0.50476
7 0.35984 0.37544 57 0.58365 0.56266
8 0.49163 0.51331 58 0.60224 0.59941
9 0.55507 0.55107 59 0.59857 0.61586
10 0.62829 0.60283 60 0.66402 0.61923
11 0.62359 0.59385 61 0.63963 0.66314
12 0.62275 0.61131 62 0.55739 0.57906
13 0.62297 0.58104 63 0.55153 0.59745
14 0.527 0.57762 64 0.54297 0.56606
15 0.5259 0.54649 65 0.53748 0.53421
16 0.51742 0.52817 66 0.49346 0.51853
17 0.50561 0.52548 67 0.51989 0.50143
18 0.51751 0.49089 68 0.50286 0.54285
19 0.49181 0.49821 69 0.57935 0.54408
20 0.49949 0.53154 70 0.51685 0.466
21 0.5618 0.53768 71 0.37535 0.38682
22 0.50039 0.52424 72 0.3052 0.31489
23 0.42115 0.36236 73 0.2485 0.22702
24 0.31385 0.26561 74 0.18677 0.17983
25 0.25403 0.24648 75 0.23376 0.17302
26 0.20954 0.17983 76 0.19633 0.16765
27 0.23536 0.17068 77 0.22679 0.20861
28 0.19264 0.20642 78 0.27111 0.27921
29 0.19882 0.20313 79 0.38858 0.35508
30 0.25472 0.25769 80 0.50631 0.47088
31 0.40847 0.422 81 0.50173 0.53097
32 0.48926 0.52585 82 0.57101 0.5497
33 0.58065 0.57998 83 0.59557 0.61762
34 0.63006 0.59915 84 0.64797 0.60274
35 0.58676 0.63386 85 0.62968 0.59875
36 0.64643 0.62202 86 0.58396 0.54544
37 0.64889 0.63288 87 0.59741 0.57299
38 0.56723 0.57818 88 0.53433 0.53273
39 0.55761 0.53378 89 0.56715 0.5431
40 0.51464 0.52248 90 0.50474 0.45821
41 0.52481 0.54811 91 0.49323 0.5101
42 0.48168 0.52841 92 0.51915 0.49832
43 0.51129 0.46284 93 0.57921 0.51791
44 0.50028 0.53289 94 0.4694 0.47481
45 0.59222 0.56132 95 0.41921 0.41015
46 0.52914 0.48675 96 0.32486 0.29833
47 0.38721 0.37325 97 0.28281 0.25465
48 0.32958 0.32154 98 0.1786 0.19928
49 0.25524 0.24201 …… …… ……
50 0.2046 0.19786 168 0.2169 0.21781
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Inglese
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Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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