Predictive analytics for energy systems
Decentralized energy systems or distributed energy systems aim to locate energy production facilities closer to the site of consumption. Such systems are being widely adopted in multiple countries and they promise lower costs to the consumers. This can be achieved as the inefficiencies related to tr...
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sg-ntu-dr.10356-1566902022-04-22T05:54:25Z Predictive analytics for energy systems Tharakan, Rohan Roy Zhang Jie School of Computer Science and Engineering Mahardhika Pratama ZhangJ@ntu.edu.sg Engineering::Computer science and engineering Decentralized energy systems or distributed energy systems aim to locate energy production facilities closer to the site of consumption. Such systems are being widely adopted in multiple countries and they promise lower costs to the consumers. This can be achieved as the inefficiencies related to transmission and distribution can be reduced compared to a single central power station. We will be focusing on a subset of the energy market, which is the electricity market. The global electric power generation, transmission, and distribution market is a 3 Trillion Dollar market and decentralized systems are on the rise. However, the challenge in such a system is to predict the direction of the flow of electricity based on the supplied from the energy production units and the demand from the neighborhoods of consumers. To solve this problem, various rule-based models have been built and most of them have not been updated with the latest technologies. Such a predictive model could save a lot of cost by helping facilities take the appropriate measures to store and transmit stored electricity. This project aims to explore a deep learning-based anomaly detection method that can be used for the prediction of the direction of the flow of electricity. The method used is called Deep one-class classification and it is ideally built to detect anomalies within the dataset. Bachelor of Engineering (Computer Engineering) 2022-04-22T05:53:49Z 2022-04-22T05:53:49Z 2022 Final Year Project (FYP) Tharakan, R. R. (2022). Predictive analytics for energy systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156690 https://hdl.handle.net/10356/156690 en SCSE21-0289 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Tharakan, Rohan Roy Predictive analytics for energy systems |
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Decentralized energy systems or distributed energy systems aim to locate energy production facilities closer to the site of consumption. Such systems are being widely adopted in multiple countries and they promise lower costs to the consumers. This can be achieved as the inefficiencies related to transmission and distribution can be reduced compared to a single central power station. We will be focusing on a subset of the energy market, which is the electricity market. The global electric power generation, transmission, and distribution market is a 3 Trillion Dollar market and decentralized systems are on the rise. However, the challenge in such a system is to predict the direction of the flow of electricity based on the supplied from the energy production units and the demand from the neighborhoods of consumers. To solve this problem, various rule-based models have been built and most of them have not been updated with the latest technologies. Such a predictive model could save a lot of cost by helping facilities take the appropriate measures to store and transmit stored electricity. This project aims to explore a deep learning-based anomaly detection method that can be used for the prediction of the direction of the flow of electricity. The method used is called Deep one-class classification and it is ideally built to detect anomalies within the dataset. |
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Zhang Jie |
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Zhang Jie Tharakan, Rohan Roy |
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Final Year Project |
author |
Tharakan, Rohan Roy |
author_sort |
Tharakan, Rohan Roy |
title |
Predictive analytics for energy systems |
title_short |
Predictive analytics for energy systems |
title_full |
Predictive analytics for energy systems |
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Predictive analytics for energy systems |
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Predictive analytics for energy systems |
title_sort |
predictive analytics for energy systems |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/156690 |
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