Commercial photovoltaic performance optimization using genetic algorithms

Manjeevan Seera; Choo Jun Tan; Kuldeep Kaur Randhawa; Kok-Keong Chong

doi:10.1007/978-981-19-1434-8_6

Commercial photovoltaic performance optimization using genetic algorithms

Manjeevan Seera
, Choo Jun Tan
, Kuldeep Kaur Randhawa
, Kok-Keong Chong

Malaysia School of Business

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

Abstract

The irradiance from the sun or solar spectral irradiance can have significant variance in different locations due to the latitude, humidity, and cosine effect of incident sunlight. The performance of the outdoor photovoltaic (PV) modules is greatly influenced by the solar spectrum. In this study, the effects of the local spectral irradiance on outdoor PV modules are of interest. With similar irradiance and operating temperature, the performances of the PV modules at different locations differ as compared with that of the benchmark AM1.5G results. In order to predict the actual PV module performance under local climate conditions, two locations in Peninsular Malaysia are considered. Twelve types of solar PV modules from different manufacturers and materials are analysed. Two sets of experiments were conducted using variants of Genetic Algorithms, where the Power Conversion Efficiency (PCE) at different irradiance levels is first considered. Results from the study show that there is a gap between the AM1.5G results and the outdoor results from the locations being analysed.

Original language	English
Title of host publication	Artificial Intelligence and Environmental Sustainability
Subtitle of host publication	Challenges and Solutions in the Era of Industry 4.0
Editors	Hui Lin Ong, Ruey-an Doong, Raouf Naguib, Chee Peng Lim, Atulya K. Nagar
Place of Publication	Singapore Singapore
Publisher	Springer
Chapter	6
Pages	117-130
Number of pages	14
Edition	1st
ISBN (Electronic)	9789811914348
ISBN (Print)	9789811914331
DOIs	https://doi.org/10.1007/978-981-19-1434-8_6
Publication status	Published - 2022

Publication series

Name	Algorithms for Intelligent Systems
Publisher	Springer
ISSN (Print)	2524-7565
ISSN (Electronic)	2524-7573

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Genetic algorithm
Optimization
Photovoltaic module
Power conversion efficiency

Access to Document

10.1007/978-981-19-1434-8_6

Cite this

Seera, M., Tan, C. J., Randhawa, K. K., & Chong, K.-K. (2022). Commercial photovoltaic performance optimization using genetic algorithms. In H. L. Ong, R. Doong, R. Naguib, C. P. Lim, & A. K. Nagar (Eds.), Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0 (1st ed., pp. 117-130). (Algorithms for Intelligent Systems). Springer. https://doi.org/10.1007/978-981-19-1434-8_6

Seera, Manjeevan ; Tan, Choo Jun ; Randhawa, Kuldeep Kaur et al. / Commercial photovoltaic performance optimization using genetic algorithms. Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0. editor / Hui Lin Ong ; Ruey-an Doong ; Raouf Naguib ; Chee Peng Lim ; Atulya K. Nagar. 1st. ed. Singapore Singapore : Springer, 2022. pp. 117-130 (Algorithms for Intelligent Systems).

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abstract = "The irradiance from the sun or solar spectral irradiance can have significant variance in different locations due to the latitude, humidity, and cosine effect of incident sunlight. The performance of the outdoor photovoltaic (PV) modules is greatly influenced by the solar spectrum. In this study, the effects of the local spectral irradiance on outdoor PV modules are of interest. With similar irradiance and operating temperature, the performances of the PV modules at different locations differ as compared with that of the benchmark AM1.5G results. In order to predict the actual PV module performance under local climate conditions, two locations in Peninsular Malaysia are considered. Twelve types of solar PV modules from different manufacturers and materials are analysed. Two sets of experiments were conducted using variants of Genetic Algorithms, where the Power Conversion Efficiency (PCE) at different irradiance levels is first considered. Results from the study show that there is a gap between the AM1.5G results and the outdoor results from the locations being analysed.",

keywords = "Genetic algorithm, Optimization, Photovoltaic module, Power conversion efficiency",

author = "Manjeevan Seera and Tan, \{Choo Jun\} and Randhawa, \{Kuldeep Kaur\} and Kok-Keong Chong",

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language = "English",

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Seera, M, Tan, CJ, Randhawa, KK & Chong, K-K 2022, Commercial photovoltaic performance optimization using genetic algorithms. in HL Ong, R Doong, R Naguib, CP Lim & AK Nagar (eds), Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0. 1st edn, Algorithms for Intelligent Systems, Springer, Singapore Singapore, pp. 117-130. https://doi.org/10.1007/978-981-19-1434-8_6

Commercial photovoltaic performance optimization using genetic algorithms. / Seera, Manjeevan; Tan, Choo Jun; Randhawa, Kuldeep Kaur et al.
Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0. ed. / Hui Lin Ong; Ruey-an Doong; Raouf Naguib; Chee Peng Lim; Atulya K. Nagar. 1st. ed. Singapore Singapore: Springer, 2022. p. 117-130 (Algorithms for Intelligent Systems).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

TY - CHAP

T1 - Commercial photovoltaic performance optimization using genetic algorithms

AU - Seera, Manjeevan

AU - Tan, Choo Jun

AU - Randhawa, Kuldeep Kaur

AU - Chong, Kok-Keong

PY - 2022

Y1 - 2022

N2 - The irradiance from the sun or solar spectral irradiance can have significant variance in different locations due to the latitude, humidity, and cosine effect of incident sunlight. The performance of the outdoor photovoltaic (PV) modules is greatly influenced by the solar spectrum. In this study, the effects of the local spectral irradiance on outdoor PV modules are of interest. With similar irradiance and operating temperature, the performances of the PV modules at different locations differ as compared with that of the benchmark AM1.5G results. In order to predict the actual PV module performance under local climate conditions, two locations in Peninsular Malaysia are considered. Twelve types of solar PV modules from different manufacturers and materials are analysed. Two sets of experiments were conducted using variants of Genetic Algorithms, where the Power Conversion Efficiency (PCE) at different irradiance levels is first considered. Results from the study show that there is a gap between the AM1.5G results and the outdoor results from the locations being analysed.

AB - The irradiance from the sun or solar spectral irradiance can have significant variance in different locations due to the latitude, humidity, and cosine effect of incident sunlight. The performance of the outdoor photovoltaic (PV) modules is greatly influenced by the solar spectrum. In this study, the effects of the local spectral irradiance on outdoor PV modules are of interest. With similar irradiance and operating temperature, the performances of the PV modules at different locations differ as compared with that of the benchmark AM1.5G results. In order to predict the actual PV module performance under local climate conditions, two locations in Peninsular Malaysia are considered. Twelve types of solar PV modules from different manufacturers and materials are analysed. Two sets of experiments were conducted using variants of Genetic Algorithms, where the Power Conversion Efficiency (PCE) at different irradiance levels is first considered. Results from the study show that there is a gap between the AM1.5G results and the outdoor results from the locations being analysed.

KW - Genetic algorithm

KW - Optimization

KW - Photovoltaic module

KW - Power conversion efficiency

U2 - 10.1007/978-981-19-1434-8_6

DO - 10.1007/978-981-19-1434-8_6

M3 - Chapter (Book)

SN - 9789811914331

T3 - Algorithms for Intelligent Systems

SP - 117

EP - 130

BT - Artificial Intelligence and Environmental Sustainability

A2 - Ong, Hui Lin

A2 - Doong, Ruey-an

A2 - Naguib, Raouf

A2 - Lim, Chee Peng

A2 - Nagar, Atulya K.

PB - Springer

CY - Singapore Singapore

ER -

Seera M, Tan CJ, Randhawa KK, Chong KK. Commercial photovoltaic performance optimization using genetic algorithms. In Ong HL, Doong R, Naguib R, Lim CP, Nagar AK, editors, Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0. 1st ed. Singapore Singapore: Springer. 2022. p. 117-130. (Algorithms for Intelligent Systems). doi: 10.1007/978-981-19-1434-8_6