Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach
Autor: | R. K Mehta, Benjamin A. Shimray, Kh. Manglem Singh, Thongam Khelchandra |
---|---|
Rok vydání: | 2017 |
Předmět: |
Article Subject
General Computer Science Operations research Power station Computer science 020209 energy General Mathematics Site selection India Context (language use) 02 engineering and technology Environment lcsh:Computer applications to medicine. Medical informatics lcsh:RC321-571 Genetic algorithm 0202 electrical engineering electronic engineering information engineering Humans Environmental impact assessment lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Hydropower business.industry General Neuroscience General Medicine Models Theoretical Ranking Multilayer perceptron lcsh:R858-859.7 020201 artificial intelligence & image processing Neural Networks Computer business Algorithms Research Article Power Plants |
Zdroj: | Computational Intelligence and Neuroscience, Vol 2017 (2017) Computational Intelligence and Neuroscience |
ISSN: | 1687-5273 1687-5265 |
Popis: | Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India. |
Databáze: | OpenAIRE |
Externí odkaz: |