Residential Load Forecasting and Dynamic Demand Side  Management

Rahman, Md. Saifur; Aziz, Md. Sifat; Baki, Abdullah Al

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dc.contributor.author	Rahman, Md. Saifur
dc.contributor.author	Aziz, Md. Sifat
dc.contributor.author	Baki, Abdullah Al
dc.date.accessioned	2025-02-28T06:37:08Z
dc.date.available	2025-02-28T06:37:08Z
dc.date.issued	2024-07-25
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dc.identifier.uri	http://hdl.handle.net/123456789/2328
dc.description	Supervised by Mr. Md. Arif Hossain, Assistant Professor, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024	en_US
dc.description.abstract	This paper optimizes home energy consumption using advanced load forecasting and dynamic demand side management (DSM) approaches for Bangladesh's changing energy landscape. Grid resilience, peak demand reduction, and energy efficiency are priorities. Long Short-Term Memory (LSTM) neural networks use historical data and weather variables to forecast energy usage. Univariate and multivariate analyses are performed, with multivariate models predicting load needs better. The research examines load-shifting solutions to balance energy usage and reduce grid stress during peak periods. This study assesses the cost-effectiveness of DSM interventions. The research shows that time-of-use tariffs and optimization algorithms can reduce costs and enhance load factors. A comprehensive literature evaluation highlights existing DSM and load forecasting methodologies and identifies critical research gaps. Optimized load-shifting lowers consumer prices and improves energy grid stability and efficiency. The findings imply that enhanced forecasting and DSM tactics can boost economic and environmental performance. The study stresses the need to use current engineering tools and approaches to produce sustainable energy solutions that promote ethical and social well-being. Policymakers and suppliers seeking energy sustainability and resilience in Bangladesh can learn from this research.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	DSM, LSTM, ToU tariff, Load shifting, Load forecasting	en_US
dc.title	Residential Load Forecasting and Dynamic Demand Side Management	en_US
dc.type	Thesis	en_US