Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.
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Brac University
2021
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Truy cập trực tuyến: | http://hdl.handle.net/10361/14795 |
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10361-147952021-07-13T21:01:23Z Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model Miraz, Kazi Farhan Al Biswas, Kallol Rudra, MD. Rakin Yousuf Huda, A.S. Nazmul Department of Electrical and Electronic Engineering, Brac University Data handling GMDH Time series analysis ANN Data pre processing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-42). The purpose of this work is to forecast the electricity demand of Bangladesh using the Group Method of Data Handling (GMDH). Electricity is one of the key variables in ensuring economic growth and a higher standard of life. For a developing country like Bangladesh, it is very important to have a near accurate forecast of electricity demand for future planning. After careful preparation of a time series data set of GDP, GNI, CO2 emission, ambient temperature, the data sheet was fed into the GMDH model and an acceptable forecast was found. GMDH Shell software is used for the application of GMDH algorithm and time series analysis.This purpose is also resolved by deep learning and ANN application.High extensive correlation among features and pre processing could make fruitful in optimal prediction Kazi Farhan Al Miraz Kallol Biswas MD. Rakin Yousuf Rudra B. Electrical and Electronic Engineering 2021-07-13T15:54:52Z 2021-07-13T15:54:52Z 2021 2021-01 Thesis ID: 14321047 ID: 15121002 ID: 16321092 http://hdl.handle.net/10361/14795 en_US 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. 42 Pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
language |
en_US |
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Data handling GMDH Time series analysis ANN Data pre processing. |
spellingShingle |
Data handling GMDH Time series analysis ANN Data pre processing. Miraz, Kazi Farhan Al Biswas, Kallol Rudra, MD. Rakin Yousuf Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. |
author2 |
Huda, A.S. Nazmul |
author_facet |
Huda, A.S. Nazmul Miraz, Kazi Farhan Al Biswas, Kallol Rudra, MD. Rakin Yousuf |
format |
Thesis |
author |
Miraz, Kazi Farhan Al Biswas, Kallol Rudra, MD. Rakin Yousuf |
author_sort |
Miraz, Kazi Farhan Al |
title |
Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
title_short |
Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
title_full |
Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
title_fullStr |
Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
title_full_unstemmed |
Forecasting electricity demand of Bangladesh and its relation with physical and natural variables using group method of data handling model |
title_sort |
forecasting electricity demand of bangladesh and its relation with physical and natural variables using group method of data handling model |
publisher |
Brac University |
publishDate |
2021 |
url |
http://hdl.handle.net/10361/14795 |
work_keys_str_mv |
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