Short term load forecasting based on Internet of Things (IoT)
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Idioma: | English |
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BRAC University
2018
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| Acesso em linha: | http://hdl.handle.net/10361/10170 |
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10361-10170 |
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10361-101702022-01-26T10:13:20Z Short term load forecasting based on Internet of Things (IoT) Uz Zaman, Mahdi Islam, Anisul Sultana, Nahid Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University LSTM SVR Internet of Things (IOT) This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-38). In the era of internet every device is getting connected to the internet. In this paper, we have assumed that IoT devices can share their power consumption history. Based on data points collected from real world environment we have conducted experiments to show that IoT can be used as a reliable backbone of a short term load forecasting system. In the experiment four machine learning algorithms Long Short-term Memory (LSTM), Support Vector Machines Regression (SVR), Decision Forest Regression with AdaBoost and Nearest Neighbors Regression were used to analyze the performance of the load forecasting system. In the experiment Long Short Term Memory Network has given comparatively better result than other three machine learning algorithm with a root mean square error of 1.82. Mahdi Uz Zaman Anisul Islam Nahid Sultana B. Computer Science and Engineering 2018-05-20T06:00:59Z 2018-05-20T06:00:59Z 2018 2018-04 Thesis ID 14101263 ID 13201044 ID 14301139 http://hdl.handle.net/10361/10170 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. 38 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
LSTM SVR Internet of Things (IOT) |
| spellingShingle |
LSTM SVR Internet of Things (IOT) Uz Zaman, Mahdi Islam, Anisul Sultana, Nahid Short term load forecasting based on Internet of Things (IoT) |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Uz Zaman, Mahdi Islam, Anisul Sultana, Nahid |
| format |
Thesis |
| author |
Uz Zaman, Mahdi Islam, Anisul Sultana, Nahid |
| author_sort |
Uz Zaman, Mahdi |
| title |
Short term load forecasting based on Internet of Things (IoT) |
| title_short |
Short term load forecasting based on Internet of Things (IoT) |
| title_full |
Short term load forecasting based on Internet of Things (IoT) |
| title_fullStr |
Short term load forecasting based on Internet of Things (IoT) |
| title_full_unstemmed |
Short term load forecasting based on Internet of Things (IoT) |
| title_sort |
short term load forecasting based on internet of things (iot) |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10170 |
| work_keys_str_mv |
AT uzzamanmahdi shorttermloadforecastingbasedoninternetofthingsiot AT islamanisul shorttermloadforecastingbasedoninternetofthingsiot AT sultananahid shorttermloadforecastingbasedoninternetofthingsiot |
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