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|Title:||Real-time microgrid optimization algorithm||Authors:||Khoo, Ding Yuan||Keywords:||Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Khoo, D. Y. (2021). Real-time microgrid optimization algorithm. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149065||Project:||B1195 - 201||Abstract:||Microgrid, a local energy provider, to self-provide and resilient system. Microgrids can be tailored per user, regardless standalone or grid tied. Many microcontrollers (PLCs) are used to generate data for feedback and control. The data produced can vary from reading load profile of users, detecting extra load consumption and even used to distribute the energy generated smartly. Except, the way the microcontroller worked is through lots of the algorithms, developed through the years. One of the algorithms implemented is machine learning. Neural Network, a type of machine learning, would be a common usage for engineering related solution. Artificial Neural Network (ANN) can do classification or regression. Classification for classify types of load profile, mainly peak and non-peak load consumption. Regression, to predict trend. Regression can be split to 2 types, linear and non-linear. This paper’s aim is to generate a general ANN model that can be used as a load prediction algorithm. Mainly the algorithm must be simplified, to be utilised by PLC. The results of the model will be benchmark against a MATLAB tool-made model to prove the accuracy of the model. The project will be hand coded with the relevant mathematical equations, using MATLAB as a base, without the tools. Input and Target for the training model will be drawn from EMA’s SES 2020 statistic. The purpose of the paper is a prequel to the Real-Time Microgrid Optimization Algorithm. The results produced by this paper achieve part of the major project. The forecast algorithm will produce a predicted value that will be fed into the optimization algorithm||URI:||https://hdl.handle.net/10356/149065||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Updated on Jun 25, 2022
Updated on Jun 25, 2022
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