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PES
IEEE Members: $25.00
Non-members: $40.00Pages/Slides: 22
The combination of strict environmental policies, uncertain weather patterns, and intermittent renewable energy resources makes it increasingly challenging to predict, trade, and operate in the electricity market. This panel session brings together experts to discuss the best strategy to leverage and apply machine learning techniques in electricity markets. The panel covers applications of machine learning techniques to improve the market efficiency and market participants’ utility. Specifically, machine learning-driven algorithmic trading strategies will be covered from the perspective of market participants. Statistical learning and graph learning techniques will be presented to improve the forecast the locational marginal prices. Examples of machine learning applications will also be introduced based on ISO’s current market designs.
Chairs:
Nanpeng Yu, Hao Zhu
Primary Committee:
Analytic Methods for Power Systems (AMPS)
Sponsor Committees:
Big Data Analytics