Prediction of solar stirling power generation in smart grid by GA-ANN model

Mohammad Sameti; Mohammad Ali Jokar; Fatemeh Razi Astaraei

doi:10.1504/IJCAT.2017.082860

Prediction of solar stirling power generation in smart grid by GA-ANN model

Mohammad Sameti
, Mohammad Ali Jokar
, Fatemeh Razi Astaraei

Research output: Contribution to journal › Article › peer-review

Abstract

A model based on the feed-forward Artificial Neural Network (ANN) optimised by the Genetic Algorithm (GA) is developed in order to estimate the power of a solar Stirling heat engine in a smart grid. Genetic Algorithm is used to decide the initial weights of the neural network. The GA-ANN model is applied to predict the power of the solar Stirling heat engine from a data set reported in literature. The performance of the GA-ANN model is compared with numerical data. The results demonstrate the effectiveness of the GA-ANN model.

Original language	English
Pages (from-to)	147-157
Number of pages	11
Journal	International Journal of Computer Applications in Technology
Volume	55
Issue number	2
DOIs	https://doi.org/10.1504/IJCAT.2017.082860
Publication status	Published - 2017
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial neural network
Genetic algorithm
Prediction
Smart grid
Solar power
Stirling heat engine

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10.1504/IJCAT.2017.082860

Cite this

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title = "Prediction of solar stirling power generation in smart grid by GA-ANN model",

abstract = "A model based on the feed-forward Artificial Neural Network (ANN) optimised by the Genetic Algorithm (GA) is developed in order to estimate the power of a solar Stirling heat engine in a smart grid. Genetic Algorithm is used to decide the initial weights of the neural network. The GA-ANN model is applied to predict the power of the solar Stirling heat engine from a data set reported in literature. The performance of the GA-ANN model is compared with numerical data. The results demonstrate the effectiveness of the GA-ANN model.",

keywords = "Artificial neural network, Genetic algorithm, Prediction, Smart grid, Solar power, Stirling heat engine",

author = "Mohammad Sameti and Jokar, \{Mohammad Ali\} and Astaraei, \{Fatemeh Razi\}",

note = "Publisher Copyright: {\textcopyright}2017 Inderscience Enterprises Ltd.",

year = "2017",

doi = "10.1504/IJCAT.2017.082860",

language = "English",

volume = "55",

pages = "147--157",

journal = "International Journal of Computer Applications in Technology",

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T1 - Prediction of solar stirling power generation in smart grid by GA-ANN model

AU - Sameti, Mohammad

AU - Jokar, Mohammad Ali

AU - Astaraei, Fatemeh Razi

PY - 2017

Y1 - 2017

N2 - A model based on the feed-forward Artificial Neural Network (ANN) optimised by the Genetic Algorithm (GA) is developed in order to estimate the power of a solar Stirling heat engine in a smart grid. Genetic Algorithm is used to decide the initial weights of the neural network. The GA-ANN model is applied to predict the power of the solar Stirling heat engine from a data set reported in literature. The performance of the GA-ANN model is compared with numerical data. The results demonstrate the effectiveness of the GA-ANN model.

AB - A model based on the feed-forward Artificial Neural Network (ANN) optimised by the Genetic Algorithm (GA) is developed in order to estimate the power of a solar Stirling heat engine in a smart grid. Genetic Algorithm is used to decide the initial weights of the neural network. The GA-ANN model is applied to predict the power of the solar Stirling heat engine from a data set reported in literature. The performance of the GA-ANN model is compared with numerical data. The results demonstrate the effectiveness of the GA-ANN model.

KW - Artificial neural network

KW - Genetic algorithm

KW - Prediction

KW - Smart grid

KW - Solar power

KW - Stirling heat engine

UR - https://www.scopus.com/pages/publications/85015936099

U2 - 10.1504/IJCAT.2017.082860

DO - 10.1504/IJCAT.2017.082860

M3 - Article

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VL - 55

SP - 147

EP - 157

JO - International Journal of Computer Applications in Technology

JF - International Journal of Computer Applications in Technology

IS - 2

ER -

Prediction of solar stirling power generation in smart grid by GA-ANN model

Abstract

UN SDGs

Keywords

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