Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters
“…Equation depicts the model equation. The detailed procedure of development of the model was described in the published work by Chakraborty and Majumder …”
Section: Methodsmentioning
confidence: 99%
“…The detailed procedure of development of the model was described in the published work by Chakraborty and Majumder. 42,43 The PNN model utilized for the estimation of UEF has 1 output, 83 numbers of hidden layers with 1011 nodes, and 24 numbers of inputs. N p is the number of submodels of the polynomial neural network topology identified by the GMDH algorithm (Equation 2c).…”
Section: Feature Selection and Data Collectionmentioning
confidence: 99%
“…20,40,41 Swell has the potential to change the wave height, period, and regularity in the wave climate which may result in the irregular pattern of energy production. That is why all these factors or indicators were augmented to produce the utilization efficiency function 42,43 which was used to determine the capacity of converters in utilization of potential wave energy from the selected region. Section 3 explains the procedure adopted to represent the impact of extreme events on the capacity of WEC in extraction of available potential.…”
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
“…Equation depicts the model equation. The detailed procedure of development of the model was described in the published work by Chakraborty and Majumder …”
Section: Methodsmentioning
confidence: 99%
“…The detailed procedure of development of the model was described in the published work by Chakraborty and Majumder. 42,43 The PNN model utilized for the estimation of UEF has 1 output, 83 numbers of hidden layers with 1011 nodes, and 24 numbers of inputs. N p is the number of submodels of the polynomial neural network topology identified by the GMDH algorithm (Equation 2c).…”
Section: Feature Selection and Data Collectionmentioning
confidence: 99%
“…20,40,41 Swell has the potential to change the wave height, period, and regularity in the wave climate which may result in the irregular pattern of energy production. That is why all these factors or indicators were augmented to produce the utilization efficiency function 42,43 which was used to determine the capacity of converters in utilization of potential wave energy from the selected region. Section 3 explains the procedure adopted to represent the impact of extreme events on the capacity of WEC in extraction of available potential.…”
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
“…This method determines compromised solutions for a problem with conflicting criteria, which can help the decision makers to reach a final decision. The compromised ranking method of VIKOR consists of several steps (Chakraborty and Majumder, 2017c). The various J alternatives are denoted as a 1 , a 2 , …, a J .…”
Section: Methods Appliedmentioning
confidence: 99%
“…In another paper (Chakraborty and Majumder, 2017c), the same objective was attempted by three different MCDM methods: AHP, DEMATEL-VIKOR, and DEMATEL-VIKOR-NFSDSS. Although AHP was utilised earlier for the same purpose, ensemble methods like DEMATEL-VIKOR and DEMATEL-VIKOR-NSFDSS were not used previously for the present objective.…”
The potential of analytical hierarchy process (AHP)-rough number based compromise ranking method (also known as VIKOR) multi-criteria decision-making (MCDM) and group method of data handling (GMDH) multimodal predictive method was analysed for smart representation of 'utilisation potential' of wave energy converters from specific locations. The significant parameters were identified by considering different case studies and their influence on converter efficiency. The soft-computation methods like AHP-VIKOR and GMDH were used for the first time to find the relative priority values of the parameters. The results from the multi-method estimation model were validated with the help of multi linear regression equation and certain real-time case analysis. With an accuracy of above, 99% the ensemble MCDM-ANN model depicted reliability, which ensures the author of its wide application for real benefits like cost reduction and efficiency maximisation of converters in the utilisation of the potential wave energy of locations.
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