Главная
Study mode:
on
1
Introduction
2
What next
3
Energy security
4
Narrow predictability
5
Market setup
6
Machine learning
7
Reinforcementlearning
8
Models
9
Model
10
Policy
11
Contracts
12
Awap
13
Work of Development
14
Expectations
15
Theorem
16
Data
17
Historical Data
18
Discussion
19
Are they getting out of these credits
Description:
Explore a Reinforcement Learning Mechanism for Trading Wind Power Futures in this NYU Brooklyn Quant Experience (BQE) Seminar Series talk by Adjunct Professor Bruno Kamdem. Delve into topics such as energy security, market setup, machine learning, and reinforcement learning models. Examine the policy contracts, historical data, and development work involved in this innovative approach to wind power trading. Gain insights into narrow predictability, expectations theorem, and the implications for energy credits in this comprehensive presentation on applying advanced quantitative techniques to renewable energy markets.

A Reinforcement Learning Mechanism for Trading Wind Power Futures

New York University (NYU)
Add to list
0:00 / 0:00