Electricity market analytics for risk management purpose
The pricing of electricity is central to the market, and forecasting future electricity prices is a matter that market participants cannot avoid dealing with. Due to the special characteristics of electricity, electricity prices are influenced by many factors and show high volatility and uncertainty...
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Nanyang Technological University
2023
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sg-ntu-dr.10356-1680202023-07-07T15:51:08Z Electricity market analytics for risk management purpose Yang, Suxuan Dong Zhao Yang School of Electrical and Electronic Engineering zy.dong@ntu.edu.sg Engineering::Electrical and electronic engineering The pricing of electricity is central to the market, and forecasting future electricity prices is a matter that market participants cannot avoid dealing with. Due to the special characteristics of electricity, electricity prices are influenced by many factors and show high volatility and uncertainty, which brings great challenges to the accurate prediction of electricity prices. To improve prediction accuracy and efficiency, the actual measurement data of the Singapore electricity market is used for a case study to forecast the electricity price of next week with KNN, SVM and BPNN. By comparing their results, we can see the performance of the each to forecast electricity price, which is meaningful to risk management. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-06-06T06:50:15Z 2023-06-06T06:50:15Z 2023 Final Year Project (FYP) Yang, S. (2023). Electricity market analytics for risk management purpose. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168020 https://hdl.handle.net/10356/168020 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Yang, Suxuan Electricity market analytics for risk management purpose |
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The pricing of electricity is central to the market, and forecasting future electricity prices is a matter that market participants cannot avoid dealing with. Due to the special characteristics of electricity, electricity prices are influenced by many factors and show high volatility and uncertainty, which brings great challenges to the accurate prediction of electricity prices. To improve prediction accuracy and efficiency, the actual measurement data of the Singapore electricity market is used for a case study to forecast the electricity price of next week with KNN, SVM and BPNN. By comparing their results, we can see the performance of the each to forecast electricity price, which is meaningful to risk management. |
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Dong Zhao Yang |
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Dong Zhao Yang Yang, Suxuan |
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Final Year Project |
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Yang, Suxuan |
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Yang, Suxuan |
title |
Electricity market analytics for risk management purpose |
title_short |
Electricity market analytics for risk management purpose |
title_full |
Electricity market analytics for risk management purpose |
title_fullStr |
Electricity market analytics for risk management purpose |
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Electricity market analytics for risk management purpose |
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electricity market analytics for risk management purpose |
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Nanyang Technological University |
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2023 |
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https://hdl.handle.net/10356/168020 |
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