Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.

Détails bibliographiques
Auteurs principaux:	Islam, Md. Kamrul, Shawon, Md. Mehedi Hasan, Akter, Sumaiya, Ahmed, Sabbir
Autres auteurs:	Rahman, Md. Mosaddequr
Format:	Thèse
Langue:	English
Publié:	Brac University 2021
Sujets:	Short circuit current Temperature Wind speed Humidity Solar irradiance Cumulative electrical energy Wind power. Machine learning Photovoltaic power systems.
Accès en ligne:	http://hdl.handle.net/10361/15727

id	10361-15727
record_format	dspace
spelling	10361-157272021-12-12T21:01:35Z Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm Islam, Md. Kamrul Shawon, Md. Mehedi Hasan Akter, Sumaiya Ahmed, Sabbir Rahman, Md. Mosaddequr Department of Electrical and Electronic Engineering, Brac University Short circuit current Temperature Wind speed Humidity Solar irradiance Cumulative electrical energy Wind power. Machine learning Photovoltaic power systems. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 123-127). The objective of this study is to inspect the performance of the photovoltaic (PV) modules in different environmental conditions and to apply a machine learning algorithm for prediction analysis. Photovoltaic modules are very sensitive to weather conditions such as cloudy, rainy, sunny days. Hence, weather parameters, for example, Irradiance, temperature, humidity, air-pressure have an impact on PV modules performance. Two Mono-Silicon PV modules have been set up on a seven-storied building in Gabtoli, Dhaka to collect the environmental data. Among two PV modules, one module is cleaned regularly and the other module is not cleaned to observe the dust effect on PV modules performance. A weather station is designed using Raspberry Pi 3B+ modules where different sensors are used to collect both modules short circuit current as well as temperature, humidity, wind speed and air-pressure data. The data of the PV modules and the environmental parameters are being collected from the end of October 2019. Data from November 2019 to February 2020, are used to analyze the performance of these PV modules. Furthermore, a theoretical calculation is done to calculate the solar irradiance (Ideal and Experimental), PV modules power output and energy output. Moreover, one of the segments of machine learning that is neural network which is used to train the model based on the collected data so that a fruitful prediction can be done. An algorithm named Multi-Layer Perceptron (MLP) using Artificial Neural Network has been developed which can provide us with the PV modules energy output of a particular day or time based on the training dataset. The accuracy of the output depends on the training dataset but most importantly it depends on the correct parameters which have been shown in this study. Md. Kamrul Islam Md. Mehedi Hasan Shawon Sumaiya Akter Sabbir Ahmed B. Electrical and Electronic Engineering 2021-12-12T09:30:13Z 2021-12-12T09:30:13Z 2020 2020-06 Thesis ID 16121105 ID 16221056 ID 16321012 ID 16321003 http://hdl.handle.net/10361/15727 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 141 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Short circuit current Temperature Wind speed Humidity Solar irradiance Cumulative electrical energy Wind power. Machine learning Photovoltaic power systems.
spellingShingle	Short circuit current Temperature Wind speed Humidity Solar irradiance Cumulative electrical energy Wind power. Machine learning Photovoltaic power systems. Islam, Md. Kamrul Shawon, Md. Mehedi Hasan Akter, Sumaiya Ahmed, Sabbir Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.
author2	Rahman, Md. Mosaddequr
author_facet	Rahman, Md. Mosaddequr Islam, Md. Kamrul Shawon, Md. Mehedi Hasan Akter, Sumaiya Ahmed, Sabbir
format	Thesis
author	Islam, Md. Kamrul Shawon, Md. Mehedi Hasan Akter, Sumaiya Ahmed, Sabbir
author_sort	Islam, Md. Kamrul
title	Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
title_short	Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
title_full	Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
title_fullStr	Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
title_full_unstemmed	Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
title_sort	outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15727
work_keys_str_mv	AT islammdkamrul outdoorperformanceanalysisandpredictionofphotovoltaicmodulesusingmachinelearningalgorithm AT shawonmdmehedihasan outdoorperformanceanalysisandpredictionofphotovoltaicmodulesusingmachinelearningalgorithm AT aktersumaiya outdoorperformanceanalysisandpredictionofphotovoltaicmodulesusingmachinelearningalgorithm AT ahmedsabbir outdoorperformanceanalysisandpredictionofphotovoltaicmodulesusingmachinelearningalgorithm
_version_	1814309429937438720

Outdoor performance analysis and prediction of photovoltaic modules using machine learning algorithm

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