The artificial neural network used in the study of sensitivities in the IRIS reactor pressurizer

Costa, Samuel Pimentel; Lima, Fernando Roberto de Andrade; Lapa, Celso Marcelo Franklin; Mól, Antônio Carlos de Abreu; Lira, Carlos Alberto Brayner de Oliveira

doi:10.1016/j.pnucene.2013.03.010

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Considering a well-trained MLP model (m × n× 1), where m is the number of nodes in the input layer, n the hidden layer nodes, and 1 the output layer node, the relative amounts of input variables can be calculated by (1) [19], [63].…”

Section: Artificial Neural Network Applied In Resmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses. INDEX TERMS Microgeneration, wind power, stochastic feasibility analysis, sensitivity analysis, artificial neural networks.

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Section: Artificial Neural Network Applied In Resmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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“…Therefore, the neural network is used as an alternative way of sensitivity analysis because it considers linearity and non-linearity. It is fast, accurate, viable and e cient alternative against the traditional techniques of sensitivity analysis (Costa et al, 2013, Dilidili et al, 2011.…”

Section: Sensitivity Analysis Of Selected Variablesmentioning

confidence: 99%

Cost-Benefit Analysis and Financial Viability of Household Biogas Plant Installation in Aleta Wondo District, Southern Ethiopia

Sime

Gedefa²,

Melka³

2020

SSRN Journal

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Background: Installation of biogas plants has both costs and incomes; installation and maintenance service demand nancial costs and reduction of costs for purchasing rewood, kerosene and chemical fertilizers are bene ts or incomes. This study investigates the cost-bene t analysis and nancial viability related to biogas plant installation in Southern Ethiopia.Method: A multi-stage sampling technique was employed to select sample households. A total of 105 adopter households were selected for household survey.Results and conclusion: The installation cost took the largest share of the total cost of installation and was one of the main constraints that hindered installation. Installation increased household income by reducing the costs incurred for buying rewood, kerosene and chemical fertilizers. Relatively, lower plant size was more pro table than larger plant size. Installation under the subsidy scheme was more nancially viable at 10 % discount rate than its counterparts. The pro tability of lower plant size was more sensitive to changes in the discount rate, the level of expenditure saving and input price than larger plant size, under an assumption and without subsidy. Installation of low cost plants could more attract the engagement of a large number of rural households with low economic capacity. Besides, installation of lower plant sizes could more substantially enhance household income by saving costs incurred for buying rewood, kerosene and chemical fertilizers.

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“…By designing a well-trained MLP network model (m × n × 1), where m is the number of nodes in the input layer, n are the nodes of the hidden layer, and 1 is the output layer node, the relative importance of input variables can be calculated by Equation (5) [27,30]:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis

et al. 2019

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Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives may allow decreasing in the investment amount for wind power. Based on this, incentives programs for the production of clean energy include cheaper purchase of wind turbines, lower taxing and financing rates, can make wind power more profitable and attractive.

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The artificial neural network used in the study of sensitivities in the IRIS reactor pressurizer

Cited by 8 publications

References 4 publications

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

Cost-Benefit Analysis and Financial Viability of Household Biogas Plant Installation in Aleta Wondo District, Southern Ethiopia

Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis

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