Research on cost analysis and prediction methods for power transmission and transformation projects based on propagation models and neural networks

doi:10.3969/j.issn.2097-0706.2025.04.003

Abstract

Abstract:

Accurate prediction on the costs of transmission and substation projects is crucial for the planning and implementation of modern power systems. Traditional prediction methods often suffer from low accuracy and poor adaptability while handling quantitative prediction problems such as time series and structural analyses. To improve prediction accuracy， a cost prediction method for transmission and substation projects was proposed based on the Susceptible-Infected-Removed （SIR） epidemic model and neural networks. This method utilized the SIR model for dynamic modeling of variable costs， and fitted the model parameters with nonlinear least squares. Historical data and model parameters were then input into a Feedforward Neural Network（FNN）， and predictions were obtained through training and computation. Finally， Bayesian optimization algorithm （BOA） was employed to optimize the hyperparameters of the FNN， completing the BOA-FNN model training. The study results indicated that this prediction method achieved a mean absolute percentage error （MAPE） as low as 0.430 7%， significantly enhancing prediction accuracy with stability and reliability.

Key words: transmission and transformation engineering, project cost, epidemic model, SIR model, feedforward neural network （FNN）, Bayesian optimization algorithm（BOA）, project investment prediction

CLC Number:

TK011：TM926

LU Handong, FANG Ming, LIU Ganggang, ZHOU Yan. Research on cost analysis and prediction methods for power transmission and transformation projects based on propagation models and neural networks[J]. Integrated Intelligent Energy, 2025, 47(4): 33-40.

Figures/Tables 7

Fig.1

Table 1

Transmission and transformation project cost historical data sample

项目序号	动态总投资/ 万元	固定费用/万元	可变费用/万元	$β$	$γ$
1	6 661.00	3 730.16	2 930.84	0.51	0.60
2	4 016.74	2 209.21	1 807.53	0.48	0.57
3	4 777.53	2 627.64	2 149.89	0.51	0.60
4	8 282.74	4 721.16	3 561.58	0.51	0.60
5	5 647.46	3 106.10	2 541.36	0.51	0.60
6	7 325.89	4 102.50	3 223.39	0.51	0.60
7	5 823.97	3 203.18	2 620.79	0.51	0.60
8	7 773.03	4 430.63	3 342.40	0.51	0.60
9	7 001.40	3 920.78	3 080.62	0.51	0.60
10	7 011.41	3 926.39	3 085.02	0.51	0.60
11	7 735.36	4 409.15	3 326.20	0.51	0.60
12	7 361.03	4 122.18	3 238.85	0.51	0.60
13	8 181.84	4 663.65	3 518.19	0.51	0.60
14	5 479.16	3 013.54	2 465.62	0.51	0.60
15	7 786.15	4 438.10	3 348.04	0.51	0.60
16	5 681.04	3 124.57	2 556.47	0.51	0.60
17	9 613.33	5 575.73	4 037.60	0.51	0.60
18	3 568.85	1 962.87	1 605.98	0.48	0.57
19	3 623.77	1 993.07	1 630.70	0.48	0.57
20	7 386.62	4 136.51	3 250.11	0.51	0.60
21	5 560.26	3 058.14	2 502.12	0.51	0.60
22	7 789.83	4 440.20	3 349.63	0.51	0.60
23	8 241.64	4 697.73	3 543.91	0.51	0.60
24	6 017.76	3 369.95	2 647.81	0.51	0.60

Table 1

Fig.2

Table 2

Fig.3

Fig.4

Table 3

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评估次数	第1层隐藏层大小	第2层隐藏层大小	学习率	RMSE
1	142	43	0.000 2	177.62
2	104	146	0.051 6	142.09
3	73	10	0.085 6	190.65
4	46	50	0.000 2	267.54
5	121	81	0.099 5	53.17
6	130	14	0.099 9	158.65
7	150	94	0.098 9	366.07
8	118	145	0.049 3	83.76
9	116	105	0.099 7	671.31
10	19	141	0.040 3	28.00
11	25	137	0.047 8	110.37
12	86	75	0.091 7	614.90
13	25	146	0.039 7	81.24
14	10	144	0.000 2	294.34
15	11	143	0.067 7	80.85
16	133	78	0.096 7	81.40
17	17	142	0.011 1	99.20
18	40	142	0.095 1	155.07
19	128	81	0.001 1	144.03
20	127	150	0.000 4	163.19
21	120	138	0.000 8	141.39
22	141	142	0.097 0	2 945.59
23	123	53	0.066 6	44.89
24	10	105	0.096 1	90.08
25	23	71	0.093 5	31.98
26	11	52	0.094 1	75.72
27	24	29	0.041 8	86.07
28	10	10	0.000 4	417.95
29	36	88	0.059 9	81.64
30	52	109	0.059 3	99.11
31	58	150	0.000 2	175.15
32	32	52	0.000 2	318.94
33	111	11	0.098 8	180.85
34	90	150	0.000 2	139.16
35	150	63	0.094 3	345.21
36	20	99	0.098 6	152.94
37	133	53	0.051 7	194.09
38	115	150	0.000 1	179.85
39	62	68	0.095 2	387.44
40	114	53	0.082 9	283.39
41	123	16	0.000 1	315.45
42	74	150	0.069 8	328.53
43	43	108	0.000 1	269.39
44	144	10	0.026 3	107.01
45	94	10	0.000 7	181.98
46	56	10	0.080 6	33.39
47	44	10	0.059 2	122.36
48	60	10	0.000 1	447.62
49	37	49	0.099 4	305.43
50	51	11	0.093 4	219.58

样本序号	实际可变费用/万元	预测可变费用/万元		相对误差/%
样本序号	实际可变费用/万元	BOA-FNN	BP	BOA-FNN	BP
1	1 630.70	1 630.27	1 540.93	0.026 5	5.504 8
2	3 250.11	3 224.50	3 153.09	0.788 0	2.985 2
3	2 502.12	2 488.92	2 426.59	0.527 5	3.018 3
4	3 349.63	3 366.06	3 148.88	0.490 5	5.993 2
5	3 543.91	3 545.90	3 256.61	0.056 1	8.106 9
6	2 647.81	2 666.22	2 597.97	0.695 4	1.882 3
MAPE				0.430 7	4.581 8