Multi-variate Time-series Load Forecasting using Deep  Learning

Arnob, Saadman Sakif; Saqalain, Ahmed Syed; Sakib, Najmus Sadat

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dc.contributor.author	Arnob, Saadman Sakif
dc.contributor.author	Saqalain, Ahmed Syed
dc.contributor.author	Sakib, Najmus Sadat
dc.date.accessioned	2023-05-05T04:46:53Z
dc.date.available	2023-05-05T04:46:53Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1880
dc.description	Supervised by Prof. Dr. Ashik Ahmed, Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.	en_US
dc.description.abstract	To ensure the stable and reliable operation of a power system, load forecasting is required. Accurate forecasting leads to efficient dispatch, unit commitment, and energy security. Smart power management in the generating, transmission, and distribution network, as well as the accompanying energy demand, can be realized with accurate forecasting approaches. This paper analyses the short-term load forecasting of the Bangladesh power system. Various deep neural network models- XGBoost, LSTM, Stacked LSTM, CNN, CNN-LSTM, Time Distributed MLP, and Encoder-Decoder are used to forecast the load. The load is predicted based on previous load data and various features like temperature, Weekdays, Weekends, and Peak Business Hours are taken to ensure the accuracy of the results. This study reports the advantages and disadvantages of each model.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	XGBoost, LSTM, CNN, MLP, ML. ADF, KPSS, ACF, PACF, GBDT, Load forecasting, Deep Neural Network, DNN	en_US
dc.title	Multi-variate Time-series Load Forecasting using Deep Learning	en_US
dc.type	Thesis	en_US