By Mrinmoy Majumder, Apu K. Saha
This short highlights a singular version to determine the feasibility of any position to provide solar power. The version makes use of the most recent multi-criteria determination making strategies and synthetic neural networks to foretell the suitability of a place to maximise allocation of obtainable power for generating optimum quantity of electrical energy to be able to fulfill the call for from the marketplace. in response to the result of the case reviews extra functions are encouraged.
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Extra resources for Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques
20 5 Methodology The performance metrics like Mean Absolute Error (MAE) and Correlation (r) was used to identify the three better performing models among all the twenty four models considered for the present study. Among the selected three models best model was identiﬁed by Root Mean Square Error (RMSE), Mean Relative Error (MRE), Standard Deviation (StDev) and Covariance (Cov). An Equivalent Performance Metrics was developed in this regard. For the ﬁrst selection EPM was made equal to the ratio of Correlation and MAE.
671242 + "No. 449512 + "No. 310107 - "No. 4 Model Limitation 37 Fig. 5 Figure showing the comparison between observed and predicted output for model no. #AHAIG6 If the type of survey is changed new parameters can emerge as more important than the existing parameters. The only solution to this problem is to devise some policy so that the method type can be ﬁxed. 10 %). 38 6 Results and Discussions Fig. 6 Figure showing the distribution of residuals from observed and predicted output for model no.
In total twelve locations were selected from around the world for the feasibility analysis by the new method for installation of solar energy plants. 2. The entire methodology is depicted in Fig. 1. Fig. 1 The entire methodology Chapter 6 Results and Discussions Abstract The results from the MCDM delineated the importance of insolation parameter in site selection methodologies. The GMDH model trained with GMDH algorithm, output data transformed by Arc tangenet function and priority value retrieved from Analytical Network Process method was found to have the highest performance among the twenty four models developed for the study.