Dynamic XGBoost-based Quantile Predictor for Real-time Electricity Price Forecasting
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| Argitaratua izan da: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2023) |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , |
| Argitaratua: |
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2921282215 | ||
| 003 | UK-CbPIL | ||
| 024 | 7 | |a 10.1109/ETFG55873.2023.10407379 |2 doi | |
| 035 | |a 2921282215 | ||
| 045 | 2 | |b d20230101 |b d20231231 | |
| 084 | |a 228229 |2 nlm | ||
| 100 | 1 | |a Bao, Tianshu |u Swinburne University of Technology,VIC,Australia | |
| 245 | 1 | |a Dynamic XGBoost-based Quantile Predictor for Real-time Electricity Price Forecasting | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2023 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Conference Title: 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)Conference Start Date: 2023, Dec. 3 Conference End Date: 2023, Dec. 6 Conference Location: Wollongong, AustraliaElectricity price forecasting helps traders and market participants manage risks by determining the best strategy for bidding. Given the strong correlation between electricity prices and load consumption, there has been a growing interest in developing frameworks to forecast both electricity price and load. This work proposes a real-time electricity price forecasting model built upon an existing real-time XGBoost-based electricity load forecasting framework. Our proposed model comprises three key components: Feature Selector, XGBoost-based Quantile Predictor, and Range Controller. To train and test the prediction model, the Feature Selector preprocesses and selects the appropriate features. A machine learning enabled quantile predictor is then applied to obtain one step ahead predictions (OSAP) of electricity prices. Lastly, the Range Controller offers a training-efficient approach to adjust the prediction interval and quantile values, as well as to classify spike and normal cases using an unsupervised classification method. Upon analysis using R2, RMSE and MAE, it has been consistently demonstrated that our adaptive forecasting model exhibits more than two times improvement over a year. | |
| 653 | |a Electrical loads | ||
| 653 | |a Forecasting | ||
| 653 | |a Prediction models | ||
| 653 | |a Quantiles | ||
| 653 | |a Electricity pricing | ||
| 653 | |a Mathematical models | ||
| 653 | |a Machine learning | ||
| 653 | |a Real time | ||
| 653 | |a Energy technology | ||
| 653 | |a Controllers | ||
| 653 | |a Electricity | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Wang, Xinlin |u CSIRO,NSW,Australia | |
| 700 | 1 | |a Mahdavi, Nariman |u CSIRO,NSW,Australia | |
| 700 | 1 | |a McCarthy, Chris |u Swinburne University of Technology,VIC,Australia | |
| 700 | 1 | |a Rezazadegan, Dana |u Swinburne University of Technology,VIC,Australia | |
| 773 | 0 | |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings |g (2023) | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2921282215/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |