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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Reinforcement learning based electricity price forecasting in Blockchain based smart grid environment

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    17101301, 17101311, 17101312, 17101310_CSE.pdf (1.426Mb)
    Date
    2021
    Publisher
    Brac University
    Author
    Moti, Md Mahraj Murshalin Al
    Uddin, Rafsan Shartaj
    Anik, Abdul Hai
    Saleh, Tanzim Bin
    Metadata
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    URI
    http://hdl.handle.net/10361/14968
    Abstract
    Electricity is deeply integrated into both our modern society and the economy. However, with our ever-growing society and increasing demand for electricity, the scarcity of resources is deeply felt through load shedding in most third world countries. Moreover, since most of the world depends on electricity systems built around more than 60 years ago, they are becoming increasingly inefficient and fail to solve the problems of modern-day global challenges. A Smart grid is an intelligent electricity network that allows efficient and optimal electricity distribution from source to consumers through smart integration of power technologies, information, and telecommunication through the existing system. The current system is a one-way interaction that only supplies electricity to consumers. That limits the ability to respond to the ever-changing and rising demands of society. However, smart grids allow the exchange of electricity and information between producers and customers. A smart home will communicate with the grid and allow consumers to manage electricity usage through a smart meter efficiently, and that will also efficiently manage electricity bills. Inside a smart home, the Home Area Network (HAN), will integrate all smart appliances into one energy management system so that these appliances can adjust the run schedule to lessen the demand on electricity at peak times, therefore, lowering bills. Reinforcement learning and a decentralized local market through block-chain can be used for electricity load and price forecasting. It is possible to fine-tune parameters to increase overall distribution and performance through efficient feature selection and feature extraction methods. The use of block-chain will connect prosumers and suppliers in a secure and decentralized system that will be used to forecast usage and bills. Also, through the use of reinforcement learning techniques and the block-chain’s information, it will be possible to analyze prosumer behavior. So, the integration of block-chain and smart grids will increase flexibility and scalability, leading to an overall optimized system.
    Keywords
    Smart Grid; Block-chain; Price Forecasting; Electricity demand and supply; Smart Meter; Reinforcement Learning
     
    LC Subject Headings
    Reinforcement learning.
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 48-51).
    Department
    Department of Computer Science and Engineering, Brac University
    Type
    Thesis
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering) [1600]

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