Performance prediction of hybrid thermoelectric generator with high accuracy using artificial neural networks
| Autor: | Lazarus Godson Asirvatham, Somchai Wongwises, J. Jayakumar, Appadurai Anitha Angeline, Duraisamy Jude Hemanth |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Series (mathematics)
Artificial neural network Renewable Energy Sustainability and the Environment business.industry Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology Series and parallel circuits Temperature gradient Thermoelectric generator Software 020401 chemical engineering Control theory 0202 electrical engineering electronic engineering information engineering Performance prediction 0204 chemical engineering business MATLAB computer computer.programming_language |
| Zdroj: | Sustainable Energy Technologies and Assessments. 33:53-60 |
| ISSN: | 2213-1388 |
| Popis: | This paper presents the application of Artificial Neural Networks for the simulation of the performance parameters of a hybrid thermoelectric generator, using the artificial neural networks tool in the MATLAB software under various temperature, load and series condition. The simulated parameters (till an input heater temperature of about 250 °C) are compared with experimental results and the average error between the experimental approach and ANN based approach for all the parameter values is less than 3%. This low error value shows that the experiments need not be repeated for input temperatures above 250 °C which is quite complex. Hence, the effect of temperature gradient on the hybrid thermoelectric generator performance upto 350 °C of the hot side temperature with a single module and “N” no. of series connection have been estimated using ANN methodology. Experimental results suggest the necessity for ANN based approaches for hybrid TEG applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect