doi: 10.3934/dcdss.2020203

Mathematical model of energy conservation evaluation of passive building based on fuzzy clustering algorithm

1. 

School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China

2. 

School of Management, Henan University of Urban Construction, Pingdingshan 467000, China

3. 

School of Energy and Environmental Engineering, Hebei University of Technology Tianjin 300401, China

* Corresponding author: Lichao Jiao

Received  March 2019 Revised  May 2019 Published  December 2019

When evaluating the energy conservation effect of passive buildings, the traditional gray comprehensive evaluation model cannot comprehensively analyze the building's own factors, which leads to lower accuracy of analysis. Mathematical model of energy conservation evaluation of passive building based on fuzzy clustering algorithm is designed, which uses the analytic hierarchy process to determine the weight of the energy conservation evaluation index of passive building. The system of energy conservation indicators for passive buildings is constructed to calculate fuzzy similarities between different evaluation indicators and to calculate them. Through the clustering of the calculation results, the energy conservation evaluation results of the passive building are obtained. The experimental results show that the designed model can effectively evaluate the energy conservation effect of passive buildings, and its generalization ability is better. The average accuracy of the evaluation is as high as 98%, and the evaluation rate is as high as 96%. The average time-consuming value of the eight passive buildings is only 540.54 s. It has the advantage of high evaluation accuracy and high efficiency.

Citation: Xian Rong, Lichao Jiao, Xiangfei Kong, Guangpu Yuan. Mathematical model of energy conservation evaluation of passive building based on fuzzy clustering algorithm. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020203
References:
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show all references

References:
[1]

F. BauerU. RömerA. Fidlin and W. Seemann, Optimization of energy efficiency of walking bipedal robots by use of elastic couplings in the form of mechanical springs, Nonlinear Dynamics, 83 (2016), 1275-1301.  doi: 10.1007/s11071-015-2402-9.  Google Scholar

[2]

S. A. C. DeY. Duroc and T. P. e. a. Vuong, Quantitative evaluation of power transfer efficiency of uhf rfid passive systems, Electronics Letters, 51 (2015), 932-933.   Google Scholar

[3]

J. Deng, L. Teng D. Pan and X. Shao, Inertial effects of the semi-passive flapping foil on its energy extraction efficiency, Physics of Fluids, 27 (2015), 053103. doi: 10.1063/1.4921384.  Google Scholar

[4]

D. FotiL. Bozzo and F. LpezAlmansa, Numerical efficiency assessment of energy dissipators for seismic protection of buildings, Earthquake Engineering and Structural Dynamics, 27 (2015), 543-556.  doi: 10.1002/(SICI)1096-9845(199806)27:6<543::AID-EQE733>3.0.CO;2-9.  Google Scholar

[5]

W. Gao and W. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones, Journal of Difference Equations and Applications, 23 (2017), 100-109.  doi: 10.1080/10236198.2016.1197214.  Google Scholar

[6]

A. K. Garg and V. Janyani, Energy efficient flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network with pay as you grow deployment, Optical Engineering, 56 (2017), 026119. doi: 10.1117/1.OE.56.2.026119.  Google Scholar

[7]

Y. GongX. HongY. Lu and e. al., Passive optical interconnects at top of the rack: offering high energy efficiency for datacenters, Optics Express, 23 (2015), 7957-7970.  doi: 10.1364/OE.23.007957.  Google Scholar

[8]

C. Ipbüker, M. Valge and K. Kalbe, e. al., Case study of multiple regression as evaluation tool for the study of relationships between energy demand, air tightness, and associated factors, Journal of Energy Engineering, 143 (2016), 04016027. Google Scholar

[9]

K. Y. Jiao and L. I. Xin, A design of video system based on data-driven multitree approach, Journal of China Academy of Electronics and Information Technology, 256 (2018), 1-69.   Google Scholar

[10]

J. JinL. G. KazovskyS. Yin and e. al., A novel quasi-passive, software-defined, and energy efficient optical access network for adaptive intra-pon flow transmission, Journal of Lightwave Technology, 33 (2015), 4536-4546.   Google Scholar

[11]

J. Jin and W. Mi, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1101-1115.   Google Scholar

[12]

L. Kazmerski, Renewable and sustainable energy reviews, Renewable and Sustainable Energy Reviews, 38 (2016), 834-847.   Google Scholar

[13]

X. F. LiD. J. Wu and S. T. Wang, Flexible mode double-switch forward converter with constant magnetic-reset voltage, Journal of Power Supply, 8765 (2018), 61-71.   Google Scholar

[14]

X. J. LiuB. Wang and C. N. Bai, Risk assessment studies on the contract energy management in energy-saving reconstruction of residential buildings based on anp-grey, Construction Technology, 45 (2016), 56-61.   Google Scholar

[15]

L. MaturiR. LolliniD. Moser and e. al., Experimental investigation of a low cost passive strategy to improve the performance of building integrated photovoltaic systems, Solar Energy, 111 (2015), 288-296.  doi: 10.1016/j.solener.2014.11.001.  Google Scholar

[16]

A. A. NikoukarI. S. HwangA. T. Liem and e. al., Qos-aware energy-efficient mechanism for sleeping mode onus in enhanced epon, Photonic Network Communications, 30 (2015), 59-70.  doi: 10.1007/s11107-015-0499-x.  Google Scholar

[17]

W. PengA. MalekiM. A. Rosen and P. Azarikhah, Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: Comparison of approaches, Desalination, 442 (2018), 16-31.  doi: 10.1016/j.desal.2018.03.021.  Google Scholar

[18]

B. R. Raut and R. S. Jangid, Seismic behavior of benchmark building with semi-active variable friction dampers, Asian Journal of Civil Engineering, 16 (2015), 417-436.   Google Scholar

[19]

C. SeminiV. BarasuolT. Boaventura and e. al., Towards versatile legged robots through active impedance control, International Journal of Robotics Research, 34 (2015), 1003-1020.  doi: 10.1177/0278364915578839.  Google Scholar

[20]

J. Song, C. Yang and Q. Zhang, e. al., Energy efficiency evaluation of tree-topology 10 gigabit ethernet passive optical network and ring-topology time- and wavelength-division-multiplexed passive optical network, Optical Engineering, 54 (2015), 090502. doi: 10.1117/1.OE.54.9.090502.  Google Scholar

[21]

S. TanakaT. Usuki and T. Yu, Accurate spice model of forward–biased silicon pin mach–zehnder modulator for an energy-efficient multilevel transmitter, Journal of Lightwave Technology, 36 (2018), 1959-1969.  doi: 10.1109/JLT.2018.2797184.  Google Scholar

[22]

J. Wang and Q. Li, Design of anti surge control of centrifugal compressor, Automation and Instrumentation, 35 (2015), 9-19.   Google Scholar

[23]

J. T. Wang and D. Jin, Analysis of models for viscoelastic wave propagation, Applied Mathematics and Nonlinear Sciences, 3 (2018), 55-96.  doi: 10.21042/AMNS.2018.1.00006.  Google Scholar

[24]

T. WangW. Xue and H. B. Lv, Study on sensor fault diagnosis simulation for aircraft engine control system, Computer Simulation, 98 (2016), 19-69.   Google Scholar

[25]

J. L. WeiK. GrobeC. Sanchez and e. al., Comparison of cost- and energy-efficient signal modulations for next generation passive optical networks, Optics Express, 23 (2015), 28271-28281.  doi: 10.1364/OE.23.028271.  Google Scholar

[26]

X. Wei, H. D. Guo and C. Feng, Grey comprehensive evaluation on policies effectiveness of energy efficiency in existing buildings, Science and Technology Management Research, (2015), 77–80. Google Scholar

[27]

Q. M. Yang and Y. Fu, Calculation model and analysis on environmental impact lca of building envelope, Building Science, 32 (2016), 114-119.   Google Scholar

[28]

C. Zhang, N. Xiao and C. Chen, e. al., Energy-efficient orthogonal frequency division multiplexing-based passive optical network based on adaptive sleep-mode control and dynamic bandwidth allocation, Optical Engineering, 55 (2016), 026108. doi: 10.1117/1.OE.55.2.026108.  Google Scholar

[29]

B. ZhouF. ZhangL. Wang and e. al., Hdeer: A distributed routing scheme for energy-efficient networking, IEEE Journal on Selected Areas in Communications, 34 (2016), 1713-1727.  doi: 10.1109/JSAC.2016.2545498.  Google Scholar

Figure 1.  Evaluation results of this model
Figure 2.  evaluation speed growth rate comparison chart
Table 1.  A-B judgement matrix
A B 1 B 2 B 3 B 4
B 1 1 2 5 4
B 2 1/2 1 3 5
B 3 1/5 1/3 1 2
b 4 1/4 1/5 1/2 1
A B 1 B 2 B 3 B 4
B 1 1 2 5 4
B 2 1/2 1 3 5
B 3 1/5 1/3 1 2
b 4 1/4 1/5 1/2 1
Table 2.  Building energy efficiency passive building energy efficiency evaluation system
Total target layer $ A $ Subtarget layer $ B_i $ Weight $ WB_i $ Index layer Weight $ C_i $
Comprehensive evaluation of building energy efficiency Economic indicators $ B_1 $ 0.486 Unit cost of energy saving transformationC11 0.495
Investment cost of building energy efficiency projectsC12 0.311
Energy cost of building operationC13 0.133
Building energy efficiency gainsC14 0.061
Technical indicators $ B_2 $ 0.319 Building (layout, shape, type) energy saving technologyC21 0.444
Building envelopeC22 0.444
Refrigeration heating lighting technologyC23 0.111
Environmental indicators $ B_3 $ 0.117 Greening rateC31 0.451
Hardened pavement and shading technologyC32 0.171
Living environmentC34 0.305
Living healthy environmentC35 0.073
Area coefficientC41 0.141
Functional indicators $ B_4 $ 0.077 Structural safetyC42 0.454
Sound insulationC43 0.263
Envelope energyC44 0.141
Total target layer $ A $ Subtarget layer $ B_i $ Weight $ WB_i $ Index layer Weight $ C_i $
Comprehensive evaluation of building energy efficiency Economic indicators $ B_1 $ 0.486 Unit cost of energy saving transformationC11 0.495
Investment cost of building energy efficiency projectsC12 0.311
Energy cost of building operationC13 0.133
Building energy efficiency gainsC14 0.061
Technical indicators $ B_2 $ 0.319 Building (layout, shape, type) energy saving technologyC21 0.444
Building envelopeC22 0.444
Refrigeration heating lighting technologyC23 0.111
Environmental indicators $ B_3 $ 0.117 Greening rateC31 0.451
Hardened pavement and shading technologyC32 0.171
Living environmentC34 0.305
Living healthy environmentC35 0.073
Area coefficientC41 0.141
Functional indicators $ B_4 $ 0.077 Structural safetyC42 0.454
Sound insulationC43 0.263
Envelope energyC44 0.141
Table 3.  Evaluation results of this model
Serial number The model evaluation results are presented in this paper Actual energy consumption grade
1
2 Ⅱ partial Ⅲ Ⅱ partial Ⅲ
3
4 Ⅲ partial Ⅱ Ⅲ partial Ⅱ
5 Ⅳ partial Ⅲ Ⅳ partial Ⅲ
6
7 Ⅲ partial Ⅱ Ⅳ Ⅲ partial Ⅱ Ⅳ
8
Serial number The model evaluation results are presented in this paper Actual energy consumption grade
1
2 Ⅱ partial Ⅲ Ⅱ partial Ⅲ
3
4 Ⅲ partial Ⅱ Ⅲ partial Ⅱ
5 Ⅳ partial Ⅲ Ⅳ partial Ⅲ
6
7 Ⅲ partial Ⅱ Ⅳ Ⅲ partial Ⅱ Ⅳ
8
Table 4.  Results of evaluation accuracy of this model
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
Passive building 1 95 97 97 96 97 98 98 98 97
Passive building 2 96 95 97 97 97 98 98 99 97
Passive building 3 99 99 98 96 98 99 95 96 98
Passive building 4 95 97 96 98 99 99 98 98 98
Passive building 5 99 99 95 97 98 99 97 95 97
Passive building 6 98 98 99 98 98 99 95 95 98
Passive building 7 97 98 98 98 98 98 98 98 98
Passive building 8 99 95 98 99 95 98 99 95 97
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
Passive building 1 95 97 97 96 97 98 98 98 97
Passive building 2 96 95 97 97 97 98 98 99 97
Passive building 3 99 99 98 96 98 99 95 96 98
Passive building 4 95 97 96 98 99 99 98 98 98
Passive building 5 99 99 95 97 98 99 97 95 97
Passive building 6 98 98 99 98 98 99 95 95 98
Passive building 7 97 98 98 98 98 98 98 98 98
Passive building 8 99 95 98 99 95 98 99 95 97
Table 5.  Grey comprehensive evaluation model accuracy rate results
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
1 85 87 87 86 87 88 88 88 87
Passive building 2 86 85 87 87 87 88 88 88 87
Passive building 3 88 88 88 86 88 88 85 86 87
Passive building 4 85 87 86 88 88 88 88 88 87
Passive building 5 88 88 85 87 88 88 87 85 87
Passive building 6 88 88 88 88 88 88 85 85 87
Passive building 7 87 88 88 88 88 88 88 88 88
Passive building 8 88 85 88 88 85 88 88 85 87
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
1 85 87 87 86 87 88 88 88 87
Passive building 2 86 85 87 87 87 88 88 88 87
Passive building 3 88 88 88 86 88 88 85 86 87
Passive building 4 85 87 86 88 88 88 88 88 87
Passive building 5 88 88 85 87 88 88 87 85 87
Passive building 6 88 88 88 88 88 88 85 85 87
Passive building 7 87 88 88 88 88 88 88 88 88
Passive building 8 88 85 88 88 85 88 88 85 87
Table 6.  Energy management risk assessment model accuracy rate results
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
Passive building 1 65 61 64 61 61 62 59 64 62
Passive building 2 65 62 64 65 63 63 64 56 63
Passive building 3 58 58 61 62 58 61 59 56 59
Passive building 4 55 55 56 58 58 58 58 58 57
Passive building 5 58 57 57 57 58 58 57 55 57
Passive building 6 58 58 49 54 58 57 56 55 56
Passive building 7 67 58 56 71 46 58 58 58 59
Passive building 8 88 45 54 51 47 48 48 67 56
Experimental object Number of times/times (%) Mean value (%)
1 2 3 4 5 6 7 8
Passive building 1 65 61 64 61 61 62 59 64 62
Passive building 2 65 62 64 65 63 63 64 56 63
Passive building 3 58 58 61 62 58 61 59 56 59
Passive building 4 55 55 56 58 58 58 58 58 57
Passive building 5 58 57 57 57 58 58 57 55 57
Passive building 6 58 58 49 54 58 57 56 55 56
Passive building 7 67 58 56 71 46 58 58 58 59
Passive building 8 88 45 54 51 47 48 48 67 56
Table 7.  Grey comprehensive evaluation model evaluation time consuming
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Mean value
1 168 168 167.5 167.4 167.73
2 336 336 335 334.8 335.45
3 504 504 502.5 502.2 503.18
4 672 672 670 669.6 670.9
5 840 840 837.5 837 838.63
6 1008 1008 1005 1004.4 1006.35
7 1176 1176 1172.5 1171.8 1174.08
8 1344 1344 1340 1339.2 1341.8
Mean value 756 756 753.75 753.3 754.765
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Mean value
1 168 168 167.5 167.4 167.73
2 336 336 335 334.8 335.45
3 504 504 502.5 502.2 503.18
4 672 672 670 669.6 670.9
5 840 840 837.5 837 838.63
6 1008 1008 1005 1004.4 1006.35
7 1176 1176 1172.5 1171.8 1174.08
8 1344 1344 1340 1339.2 1341.8
Mean value 756 756 753.75 753.3 754.765
Table 8.  Time consuming of model evaluation in this paper
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Time per request
1 120 120 120 120.5 120.13
2 240 240 240 241 240.25
3 360 360 360 361.5 360.13
4 480 480 480 482 480.5
5 600 600 600 602.5 600.63
6 720 720 720 723 720.75
7 840 840 840 843.5 840.88
8 960 960 960 964 961
Mean value 540 540 540 542.25 540.54
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Time per request
1 120 120 120 120.5 120.13
2 240 240 240 241 240.25
3 360 360 360 361.5 360.13
4 480 480 480 482 480.5
5 600 600 600 602.5 600.63
6 720 720 720 723 720.75
7 840 840 840 843.5 840.88
8 960 960 960 964 961
Mean value 540 540 540 542.25 540.54
Table 9.  Energy management risk assessment model evaluation time consuming
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Time per request
1 144 144.5 144.5 1144.5 144.38
2 288 289 289 289 288.75
3 432 433.5 433.5 433.5 433.13
4 576 578 578 578 577.5
5 720 722.5 722.5 722.5 721.88
6 864 867 867 867 866.25
7 1008 1011.5 1011.5 1011.5 1010.63
8 1152 1156 1156 1156 1155
Mean value 648 650.25 650.25 775.25 649.69
Passive building quantity/individual Time consuming /s
First times Second times Third times Fourth times Time per request
1 144 144.5 144.5 1144.5 144.38
2 288 289 289 289 288.75
3 432 433.5 433.5 433.5 433.13
4 576 578 578 578 577.5
5 720 722.5 722.5 722.5 721.88
6 864 867 867 867 866.25
7 1008 1011.5 1011.5 1011.5 1010.63
8 1152 1156 1156 1156 1155
Mean value 648 650.25 650.25 775.25 649.69
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