Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System

doi:10.16182/j.issn1004731x.joss.21-1304

Abstract

Abstract:

Aiming at the traditional energy consumption modeling methods of discrete manufacturing system being difficult to adapt to the complexity and variability of working conditions, an online dynamic energy consumption modeling method based on real-time data is proposed. The energy consumption affecting factors are determined by analyzing the operation mechanism of the discrete manufacturing system and equipment. An online sequential extreme learning machine algorithm that can dynamically adjust the number of hidden layer nodes is proposed to construct the energy consumption model. The real-time data can update the model quickly. Bernstein's inequality is introduced to improve the model data screening ability. The simulation experiment and the comparison show that the method has better regression accuracy, smaller prediction error and shorter modeling time, and can be applied to the dynamic modeling and online prediction scenarios of energy consumption of discrete manufacturing systems.

Key words: discrete manufacturing, prediction of energy consumption, online sequential extreme learning machine, Bernstein inequality

CLC Number:

TP391.9

Wei Chen, Yan Wang, Zhicheng Ji. Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System[J]. Journal of System Simulation, 2023, 35(4): 760-772.

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Table 2

Algorithm test results when N2=1

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.887	3.052	3.641	0.891	3.065	0.188	0.891	3.016	0.250	0.923	2.664	0.266
Real Estate Valuation	0.504	7.929	1.516	0.501	7.854	0.163	0.571	7.454	0.188	0.637	7.113	0.227
Air Quality	0.957	67.998	340.980	0.949	66.619	1.297	0.900	66.139	1.318	0.961	63.845	2.406
Yacht Hydrodynamics	0.741	5.114	0.984	0.721	8.175	0.125	0.658	7.630	0.141	0.766	4.914	0.198
SkillCraft1 Master Table	0.974	9.951	40.875	0.942	9.744	0.500	0.838	8.592	0.503	0.986	7.283	0.641

Table 2

Table 3

Algorithm test results when N2=2

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.885	3.050	1.984	0.876	3.152	0.250	0.880	3.116	0.266	0.916	2.653	0.322
Real Estate Valuation	0.541	7.620	0.766	0.466	8.032	0.234	0.592	7.326	0.250	0.645	6.965	0.297
Air Quality	0.954	68.951	184.200	0.953	70.112	1.313	0.937	69.312	1.516	0.962	63.085	3.469
Yacht Hydrodynamics	0.738	6.896	0.547	0.632	7.830	0.141	0.641	7.758	0.172	0.826	5.838	0.188
SkillCraft1 Master Table	0.961	10.864	24.750	0.954	10.588	0.281	0.907	12.495	0.297	0.971	8.711	0.516

Table 3

Table 4

Algorithm test results when N2=3

数据集	ELM			OSELM			IOSELM			BI-IOSELM
数据集	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s	$R 2$	RMSE	t/s
Energy Efficiency	0.889	3.000	1.594	0.876	3.157	0.219	0.882	3.094	0.234	0.902	2.835	0.313
Real Estate Valuation	0.520	7.321	0.578	0.517	7.767	0.203	0.281	7.271	0.225	0.638	7.018	0.291
Air Quality	0.956	67.393	122.530	0.952	69.556	0.329	0.953	69.225	0.391	0.960	64.476	2.984
Yacht Hydrodynamics	0.786	6.363	0.453	0.547	8.419	0.115	0.632	7.830	0.121	0.805	6.114	0.126
SkillCraft1 Master Table	0.964	9.652	15.844	0.968	9.217	0.219	0.892	10.038	0.328	0.969	9.069	0.507

Table 4

Table 5

Test Data

序号	$v$ /(m/min)	$f$ /(mm/r)	$a$ /mm	$n$ /(r/min)	$P$ /W
1	60	0.10	0.5	381.97	1 440.8
2	60	0.25	2.0	561.72	3 463.3
3	80	0.10	0.5	509.29	1 583.9
4	80	0.25	2.0	748.96	4 364.4
…
61	100	0.15	1.5	860.29	3 503.8
62	120	0.15	1.5	1 032.40	4 074.9
63	100	0.10	1.0	649.61	2 312.7
64	80	0.10	2.0	578.74	2 638.8

Table 5

Fig. 3

Fig. 4

Table 6

Model test results

算法模型	$R 2$	RMSE	t/s
DE-SVR	0.982 8	130.980	2.312 5
BPNN	0.978 1	121.100	5.203 1
CART	0.853 5	356.460	0.718 8
ELM	0.995 8	64.833	0.171 9
OSELM	0.989 9	99.627	0.078 1
IOSELM	0.989 3	102.800	0.125 0
BI-IOSELM	0.997 4	50.503	0.140 6

Table 6

Table 7

Fig. 5

Table 8

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算法模型	样本预测值
算法模型	最大相对误差	平均相对误差
DE-SVR	14.84	3.70
BPNN	22.80	5.74
CART	34.34	10.12
ELM	9.47	1.90
OSELM	11.41	2.74
IOSELM	13.21	3.26
BI-IOSELM	5.33	1.39

测试算法	测试数据1		测试数据2		测试数据3		测试数据4
测试算法	预测值	误差/%	预测值	误差/%	预测值	误差/%	预测值	误差/%
DE-SVR	3 593.5	2.56	3 868.9	5.06	2 226.7	3.72	2 758.8	4.55
BPNN	3 280.3	6.38	3 820.3	6.25	2 272.0	1.76	3 156.0	19.60
CART	4 666.7	33.18	4 666.7	14.52	2 135.2	7.68	3 266.5	23.79
ELM	3 598.6	2.71	4 099.5	0.60	2 296.4	0.70	2 513.6	4.74
OSELM	2 859.1	18.40	3 317.2	18.59	2 691.0	16.35	3 158.6	19.70
IOSELM	3 057.7	12.73	4 147.6	1.78	2 691.9	16.40	3 055.3	15.78
BI-IOSELM	3 541.1	1.06	4 058.3	0.41	2 328.1	0.67	2 632.4	0.24

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