Data Science Seminar: Saifur Rahman on Deep Neural Network Methods for Predicting Electricity Prices
When & Where
August 22
12:00 PM - 1:00 PM
WebEx ( View in Google Map)
Contact
- Scott Dyson
- scott.b.dyson@uth.tmc.edu
Event Description
Where: WebEx (See website link)
When: Aug 22, 12pm - 1pm
Speaker: Saifur Rahman of the University of Minnesota
Abstract:
Day-ahead electricity price forecasting is a critical research area that revolves around predicting prices in wholesale electricity markets. While significant progress has been made in energy price forecasting, the existence of a state-of-the-art method for accurately predicting prices in the US energy market remains a topic of debate. The wholesale and retail markets in the USA greatly value improvements in the accuracy of electricity price forecasts. It is evident that renewable energy sources have become increasingly influential in the US power market, enhancing their effectiveness. However, existing forecasting models exhibit limitations, such as inadequate consideration of the impact of renewable energy and insufficient feature selection. Furthermore, the reproducibility of research, transparent depiction of input features, and the inclusion of renewable resources in electricity price forecasting are either lacking or loosely attempted.
Password: dvFjPCp5p86
Event Site Link
https://uthealth.webex.com/uthealth/j.php?MTID=m2287c00211a710f5b7c8a8a5fcba5fd2
Additional Information
Where: WebEx (See website link)
When: Aug 22, 12pm - 1pm
Speaker: Saifur Rahman of the University of Minnesota
Abstract:
Day-ahead electricity price forecasting is a critical research area that revolves around predicting prices in wholesale electricity markets. While significant progress has been made in energy price forecasting, the existence of a state-of-the-art method for accurately predicting prices in the US energy market remains a topic of debate. The wholesale and retail markets in the USA greatly value improvements in the accuracy of electricity price forecasts. It is evident that renewable energy sources have become increasingly influential in the US power market, enhancing their effectiveness. However, existing forecasting models exhibit limitations, such as inadequate consideration of the impact of renewable energy and insufficient feature selection. Furthermore, the reproducibility of research, transparent depiction of input features, and the inclusion of renewable resources in electricity price forecasting are either lacking or loosely attempted.
Password: dvFjPCp5p86
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