Skip to main content

Advertisement

SpringerLink
Log in

Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

The rise in the demand for energy from the burgeoning population has enhanced the importance of renewable energy resources to substitute conventional energy resources. In this regard, energy extracted from ocean waves is found to be one of the most reliable but expensive alternatives. The cost of installation and monitoring of wave energy converters are the reasons why it is not a popular alternative to replace fossil fuels. One of the major reasons for higher cost lies in the subjective methods adopted to monitor or predict the wave energy potential. Also very few studies were conducted to monitor the efficiency of the converters in utilization of the available potential. The present investigation is an attempt to propose an objective, unbiased and adaptive procedure to monitor as well as estimate the utilization efficiency of the wave energy converters. The method was experimented on the coastal regions of India, and the results encourage further application of the novel method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Brooke, J. (2003). Wave energy conversion (Vol. 6). Elsevier.

  • Chakraborti, S. (2006). Parameter estimation and design considerations in prospective applications of the X chart. Journal of Applied Statistics, 33(4), 439–459.

    Article  Google Scholar 

  • Chew, X. Y., Khoo, M. B. C., Teh, S. Y., & Castagliola, P. (2015). The variable sampling interval run sum control chart. Computers & Industrial Engineering, 90, 25–38.

    Article  Google Scholar 

  • Citiroglu, K. H., & Okur, A. (2014). An approach to wave energy converter applications in Eregli on the western Black Sea coast of Turkey. Applied Energy, 135, 738–747.

    Article  Google Scholar 

  • Clémentb, A., McCullenc, P., Falcãod, A., Fiorentinoe, A., Gardnerf, F., Hammarlundg, K., et al. (2002). Wave energy in Europe: current status and perspectives. Renewable and Sustainable Energy Reviews, 6(5), 405–431.

    Article  Google Scholar 

  • Cordonnier, J., Gorintina, F., De Cagny, A., Clement, A. H., & Babarit, A. (2015). SEAREV: Case study of the development of a wave energy converter. Renewable Energy, 80, 40–52.

    Article  Google Scholar 

  • de Antonio, F. O. (2010). Wave energy utilization: A review of the technologies. Renewable and Sustainable Energy Reviews, 14(3), 899–918.

    Article  Google Scholar 

  • Gabus, A., & Fontela, E. (1973). Perceptions of the world problematique: Communication procedure, communicating with those bearing collective responsibility, DEMATEL.

  • Ganguly, A., & Patel, S. K. (2014). A teaching–learning based optimization approach for economic design of X-bar control chart. Applied Soft Computing, 30, 643–653.

    Article  Google Scholar 

  • Ghosh, S., Chakraborty, T., Saha, S., Majumder, M., & Pal, M. (2016). Development of the location suitability index for wave energy production by ANN and MCDM techniques. Renewable and Sustainable Energy Reviews, 59, 1017–1028.

    Article  Google Scholar 

  • Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32(2), 135–154.

    Article  CAS  Google Scholar 

  • Henderson, R. (2006). Design, simulation, and testing of a novel hydraulic power take-off system for the Pelamis wave energy converter. Renewable Energy, 2, 271–283.

    Article  Google Scholar 

  • Hu, X. L., Castagliola, P., Sun, J. S., & Khoo, M. B. C. (2016). Effect of measurement errors on the VSI X chart. European Journal of Industrial Engineering, 10(2), 224–242.

    Article  Google Scholar 

  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 4, 679–688.

    Article  Google Scholar 

  • Irurzun, M. A., Chaparro, M. A. E., Sinito, A. M., Gogorza, C. S. G., Lirio, J. M., Nuñez, H., et al. (2013). Preliminary relative palaeointensity record and chronology on sedimentary cores from Lake Esmeralda (Vega Island, Antarctica). Latimag Letters, 6.

  • Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517–2527.

    Article  Google Scholar 

  • Kewalramani, M. A., & Gupta, R. (2016). Group method of data handling algorithms to predict compressive strength of concrete based on absorbed extraterrestrial solar radiations. Key Engineering Materials, 689, 108–113.

    Article  Google Scholar 

  • Kleijnen, J. P. C. (1992). Sensitivity analysis of simulation experiments: regression analysis and statistical design. Mathematics and Computers in Simulation, 3, 297–315.

    Article  Google Scholar 

  • Li, Y., Duan, W., Sun, Y., & Zhang, Q. (2013). A group DEMATEL approach based on interval estimation. Journal of Convergence Information Technology, 10, 292.

    CAS  Google Scholar 

  • Li, M., Jin, L., & Wang, J. (2014). A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user’s perspective in intuitionistic fuzzy environment. Applied Soft Computing, 21, 28–37.

    Article  Google Scholar 

  • Liu, H.-C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 17, 12926–12934.

    Article  Google Scholar 

  • Majumder, M., & Saha, A. K. (2016). Feasibility model of solar energy plants by ANN and MCDM techniques. Berlin: Springer.

    Book  Google Scholar 

  • McCormick, M. E. (1981). Ocean wave energy conversion. New York: Wiley.

    Google Scholar 

  • Morabia, Z. S., Owlia, M. S., Bashiri, M., & Doroudyan, M. H. (2015). Multi-objective design of control charts with fuzzy process parameters using the hybrid epsilon constraint PSO. Applied Soft Computing, 30, 390–399.

    Article  Google Scholar 

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.

    Article  Google Scholar 

  • Mork, G., Barstow, S., Kabuth, A., & Pontes, M. T. (2010). Assessing the global wave energy potential. In ASME 2010 29th International conference on ocean, offshore and arctic engineering, (pp. 447–454). American Society of Mechanical Engineers.

  • Mrugalski, M. (2014). Robust fault detection using zonotope-based GMDH neural network (pp. 101–112). Berlin Heidelberg: Intelligent Systems in Technical and Medical Diagnostics. Springer.

    Google Scholar 

  • Najafzadeh, M., & Tafarojnoruz, A. (2010). Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environmental Earth Sciences, 2, 1–12.

    Google Scholar 

  • Narasimhan, R. (1983). An analytic approach to supplier selection. Journal of Purchasing and Supply Management, 1, 27–32.

    Google Scholar 

  • Rezaie, K., Ramiyani, S. S., Nazari-Shirkouhi, S., & Badizadeh, A. (2014). Evaluating performance of Iranian cement firms using an integrated fuzzy AHP–VIKOR method. Applied Mathematical Modelling, 38(21), 5033–5046.

    Article  Google Scholar 

  • Saaty, T. L. (1994). Fundamentals of decision making and priority theory with the AHP. Pittsburgh, PA: RWS Publications.

    Google Scholar 

  • Tam, C. M., Tong, T. K. L., & Chiu, G. W. C. (2006). Comparing non-structural fuzzy decision support system and analytical hierarchy process in decision-making for construction problems. European Journal of Operational Research, 174(2), 1317–1324.

    Article  Google Scholar 

  • Tsai, S.-B., Chien, M.-F., Xue, Y., Li, L., Jiang, X., Chen, Q., et al. (2015). Using the fuzzy DEMATEL to determine environmental performance: a case of printed circuit board industry in Taiwan. PLoS ONE, 10(6), e0129153.

    Article  Google Scholar 

  • Tseng, M.-L., & Lin, Y. H. (2009). Application of Fuzzy DEMATEL to develop a cause and effect model of municipal solid waste management in Metro Manila. Environmental Monitoring and Assessment, 158, 519–533.

    Article  Google Scholar 

  • Veigas, M., López, M., & Iglesias, G. (2014). Assessing the optimal location for a shoreline wave energy converter. Applied Energy, 132, 404–411.

    Article  Google Scholar 

  • Vining, J. G., & Muetze, A. (2007). Governmental regulation of ocean wave energy converter installations. In Industry applications conference, 2007. 42nd IAS annual meeting. Conference record of the 2007 IEEE, (pp. 749–755). IEEE.

  • Washio, Y., Osawa, H., Nagata, Y., Fujii, F., Furuyama, H., & Fujita, T. (2000). The offshore floating type wave power device “Mighty Whale”: Open Sea Tests. In The Tenth international offshore and polar engineering conference. International society of offshore and polar engineers.

  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 1, 79–82.

    Article  Google Scholar 

  • Yusuff, R. D., & Yee, K. P. (2001). A preliminary study on the potential use of the analytical hierarchical process (AHP) to predict advanced manufacturing technology (AMT) implementation. Robotics and Computer Integrated Manufacturing, 17, 421–427.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Hydro-Informatics Engineering, National Institute of Technology Agartala, West Tripura, 799046, India

    Tilottama Chakraborty & Mrinmoy Majumder

Authors
  1. Tilottama Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mrinmoy Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mrinmoy Majumder.

Appendix

Appendix

$$\begin{aligned}&N2 = -2.76416 + N1009^2 \times 0.0887725 + N3 \times 0.999995\\&N3 = -0.171508 + w15 \times 0.629221 - w15^2 \times 0.437287 + N4 \times 1.00047\\&N4 = -0.0659477 + w17 \times 0.516164 + w17 \times N5 \times 0.0357823 - w17^2 \times 0.514861 + N5 \times 0.9781\\&N5 = 7.19168 - N849 \times 2.92996 - N849 \times N6 \times 0.0350728 + N849^2 \times 0.291509 + N6 \times 1.19989\\&N6 = -0.118488 + N522 \times 0.147181 + N522 \times N7 \times 0.0465448 - N522^2 \times 0.0332651 + N7 \times 0.891672 - N7^2 \times 0.0151208\\&N7 = -0.241926 - w4 \times N8 \times 0.0880943 + w4^2 \times 0.625859 + N8 \times 1.05114\\&N8 = -0.595481 + N925 \times N9 \times 0.0133708 + N925^2 \times 0.0196041 + N9 \times 0.922148\\&N9 = 0.024995 - w10 \times 0.229829 + w10 \times N10 \times 0.0492006 + w10^2 \times 0.299619 + N10 \times 0.973327\\&N10 = -1.29417 + N623 \times 0.27857 - N623 \times N12 \times 0.0484515 + N12 \times 1.18277 + N12^2 \times 0.0090397\\&N12 = 0.289761 - N497 \times 0.465437 - N497 \times N22 \times 0.0260571 + N497^2 \times 0.0309151 + N22 \times 1.38043\\&N22 = 1.15396 - w5 \times 2.10965 + w5 \times N29 \times 0.369068 + N29 \times 0.781117 + N29^2 \times 0.00661085\\&N29 = -0.0349123 + N42 \times 0.537689 + N55 \times 0.468568\\&N55 = -0.0480321 + N72 \times 0.570288 + N128 \times 0.43832\\&N128 = -7.58468 + N752 \times 2.13375 - N752^2 \times 0.13521 + N171 \times 0.989656\\&N171 = 0.492492 + N292^2 \times 0.0260235 + N440 \times 0.839296 - N440^2 \times 0.0157626\\&N440 = 4.8973 - N529 \times 0.735168 + N529 \times N657 \times 0.281558 + N529^2 \times 0.00790615 - N657 \times 0.776674\\&N657 = -0.364707 + N691^2 \times 0.0903115 + N790^2 \times 0.0978857\\&N790 = 3.8958 + w6 \times 3.79822 - w6^2 \times 2.36449 + w7 \times 1.16794\\&N691 = 6.68679 + w14 \times 0.845645 - w20 \times 4.35088 + w20^2 \times 1.87022\\&N292 = 7.45248 - N490 \times 0.604019 + N490 \times N806 \times 0.225122 + N490^2 \times 0.0200237 - N806 \times 1.8128 + N806^2 \times 0.121524\\&N806 = 80.1224 - N929 \times 14.4294 + N929 \times N951 \times 2.77164 - N951 \times 14.3954\\&N951 = 5.4267 + w1 \times 1.34441 - w1^2 \times 0.431954 - w19 \times 0.745522\\&N929 = 5.03283 + w9 \times 1.1569\\&N490 = 10.7176 - N567 \times 2.05718 + N567 \times N630 \times 0.408916 + N567^2 \times 0.0609917 - N630 \times 1.80331 + N630^2 \times 0.0464186\\&N630 = 7.66969 + w3 \times 2.12839 - w3 \times w8 \times 2.23398 - w8 \times 7.22162 + w8^2 \times 3.19904\\&N752 = 17.0693 + N877 \times N895 \times 1.24991 - N877^2 \times 0.53792 - N895 \times 6.01448\\&N895 = 5.37887 - w15 \times 1.15123 + w15^2 \times 1.35214 - w17 \times 0.470304 + w17^2 \times 1.71652\\&N877 = 4.54893 + w2 \times 0.963564 + w9 \times 1.15805\\&N72 = -8.22867 + N725 \times 2.75005 + N725 \times N135 \times 0.035555 - N725^2 \times 0.221738 + N135 \times 0.780709\\&N135 = 0.276103 + N263 \times 0.573316 + N302 \times 0.320446 + N302^2 \times 0.00810599\\&N302 = 3.17041 - N524 \times 0.789111 + N524 \times N648 \times 0.254055 + N524^2 \times 0.0196288 - N648^2 \times 0.0592789\\&N648 = 25.0616 - N683 \times 2.33845 + N683 \times N741 \times 0.599552 - N741 \times 6.86992 + N741^2 \times 0.419435\\&N741 = 3.23494 + w4 \times 3.541 - w4^2 \times 1.99239 + w6 \times 4.14355 - w6^2 \times 2.515\\&N683 = 6.73244 + w12 \times 0.873867 - w20 \times 4.52992 + w20^2 \times 1.98509\\&N524 = 8.43845 - N590 \times 1.3758 + N590 \times N631 \times 0.392263 + N590^2 \times 0.0154597 - N631 \times 1.70513 + N631^2 \times 0.0485425\\&N590 = 8.73167 - w5 \times 12.5364 + w5^2 \times 6.66406 + w10 \times 3.28796 - w10^2 \times 2.24101\\&N263 = 1.28262 - N484 \times 0.391469 + N484 \times N764 \times 0.179864 + N484^2 \times 0.0227447\\&N484 = 12.114 - N567 \times 2.07456 + N567 \times N632 \times 0.424421 + N567^2 \times 0.0554194 - N632 \times 2.32789 + N632^2 \times 0.0865404\\&N632 = 8.4004 - w8 \times 8.56815 + w8^2 \times 3.44314 + w9^2 \times 1.17954\\&N567 = 12.3127 - w5 \times 15.3489 + w5 \times w20 \times 5.20079 + w5^2 \times 6.94 - w20 \times 5.94274 + w20^2 \times 0.849893\\&N725 = -0.0401831 + N866 \times N876 \times 0.906449 - N866^2 \times 0.359128 - N876^2 \times 0.361943\\&N876 = 4.83989 - w3 \times w9 \times 1.85021 + w3^2 \times 1.73546 + w9^2 \times 1.97783\\&N866 = 4.77751 + w1 \times 1.30485 - w1^2 \times 0.367571 - w17 \times 0.783859 + w17^2 \times 2.00117\\&N42 = -1.52554 + N1011 \times 0.271843 + N1011 \times N79 \times 0.179554\\&N79 = -0.0508932 + N179 \times 0.722969 + N179 \times N189 \times 0.0236125 - N179^2 \times 0.0226393 + N189 \times 0.280598\\&N189 = 0.520668 - N284 \times 0.104263 + N284 \times N369 \times 0.0625927 + N369 \times 0.93169 - N369^2 \times 0.0504738\\&N369 = 2.62355 - N529 \times 0.743294 + N529 \times N658 \times 0.267303 + N529^2 \times 0.0150277 - N658^2 \times 0.0567503\\&N658 = 16.144 - N696 \times 2.88922 + N696 \times N796 \times 0.695013 - N796 \times 2.8816\\&N796 = 3.94149 + w6 \times 3.69217 - w6^2 \times 2.24287 + w9 \times 1.15152\\&N696 = 6.6178 - w20 \times 3.92711 - w20 \times w22 \times 1.17472 + w20^2 \times 1.9966 + w22 \times 1.07594\\&N529 = 5.67901 - N585 \times 1.06029 + N585 \times N626 \times 0.368889 - N626 \times 1.01217\\&N626 = 7.47213 + w4 \times 2.42677 - w4 \times w8 \times 2.3349 - w8 \times 7.14317 + w8^2 \times 3.23532\\&N585 = 8.46919 + w1 \times 2.35532 - w1 \times w5 \times 2.15398 - w5 \times 11.5062 + w5^2 \times 6.68651\\&N284 = -5.97261 - N503 \times 0.645953 + N503 \times N749 \times 0.22953 + N503^2 \times 0.0211985 + N749 \times 2.98934 - N749^2 \times 0.300846\\&N749 = 6.21667 - N896 \times N917 \times 1.40492 + N896^2 \times 0.798978 - N917 \times 2.05764 + N917^2 \times 0.945203\\&N917 = 5.18947 + w3 \times w12 \times 1.7483 - w12^2 \times 0.1451\\&N896 = 4.51494 + w1 \times 1.31741 - w1^2 \times 0.403548 + w9 \times 1.12974\\&N503 = 11.3078 - N593 \times 1.72791 + N593 \times N625 \times 0.445321 + N593^2 \times 0.0215459 - N625 \times 2.40177 + N625^2 \times 0.0811518\\&N593 = 9.21221 - w5 \times 11.6542 - w5 \times w15 \times 1.53896 + w5^2 \times 6.56268 + w15^2 \times 1.20323\\&N179 = -1.15255 + N764 \times 0.226578 + N764 \times N285 \times 0.103791 + N285 \times 0.399483\\&N285 = 3.329 - N522 \times 0.994618 + N522 \times N663 \times 0.282242 + N522^2 \times 0.0257047 - N663^2 \times 0.0620511\\&N663 = 21.3855 - N702 \times 3.84933 + N702 \times N811 \times 0.865738 - N811 \times 3.8146\\&N811 = 4.05175 + w4 \times 3.21154 - w4^2 \times 1.87565 + w9 \times 1.14011\\&N702 = 7.05248 + w15^2 \times 0.333544 - w20 \times 4.57259 + w20^2 \times 2.05734\\&N764 = -0.152297 + N887 \times N906 \times 0.620071 - N887^2 \times 0.219364 - N906^2 \times 0.214096\\&N906 = 4.81564 + w2 \times 0.935259 + w3^2 \times 0.894527\\&N887 = 4.42817 + w1 \times 1.45947 - w1^2 \times 0.507466 + w7 \times 1.18133\\&N1011 = 5.33759 + w10 \times w18 \times 0.921209\\&N497 = 12.0096 - N591 \times 1.92826 + N591 \times N625 \times 0.454253 + N591^2 \times 0.0344136 - N625 \times 2.45954 + N625^2 \times 0.0817931\\&N625 = 10.8689 - w8 \times 9.44968 + w8 \times w20 \times 3.1586 + w8^2 \times 2.92758 - w20 \times 6.32235 + w20^2 \times 2.5305\\&N591 = 8.89041 - w5 \times 11.4163 - w5 \times w22 \times 1.84714 + w5^2 \times 6.49792 + w22 \times 1.39408\\&N623 = 0.369967 + N644 \times N675 \times 0.172961 - N675^2 \times 0.00593946\\&N675 = 5.98146 + w9 \times 2.38513 - w9 \times w20 \times 2.54843 - w20 \times 3.07025 + w20^2 \times 1.77879\\&N644 = 8.83895 - w8 \times 8.70193 + w8 \times w23 \times 0.555891 + w8^2 \times 3.28977 - w23^2 \times 0.291384\\&N925 = -0.10978 + N971 \times N979 \times 0.182749\\&N979 = 5.53752 + w12 \times w21 \times 1.30989 - w21 \times 0.559395\\&N971 = 5.30327 + w11 \times w14 \times 1.1084\\&N522 = 6.70488 - N586 \times 1.24506 + N586 \times N631 \times 0.404326 - N631 \times 1.20899\\&N631 = 9.86299 - w8 \times 9.72416 + w8 \times w19 \times 2.1946 + w8^2 \times 3.44417 - w19 \times 2.10455\\&N586 = 8.57683 - w5 \times 11.5011 - w5 \times w6 \times 1.517 + w5^2 \times 6.47054 + w6 \times 1.95197\\&N849 = 4.62257 + w6 \times 3.00872 + w6 \times w16 \times 1.35289 - w6^2 \times 2.2104 - w16^2 \times 0.394484\\&N1009 = 5.50885 + w11 \times w13 \times 0.281712\\&N913 = 4.71394 + w9 \times 2.00952 - w9 \times w23 \times 1.66822 + w23^2 \times 0.888132 \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, T., Majumder, M. Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters. Environ Dev Sustain 21, 199–219 (2019). https://doi.org/10.1007/s10668-017-0030-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-017-0030-x

Keywords

Search

Navigation