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As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.Sequential and reinforcement learning for demand side management by Margaux B...
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AI Explainability and Model Risk Management
- 1. Qu Speaker Series AI Explainability and Model Risk Management Dr.Anupam Datta Truera & Carnegie Mellon University 2021 Copyright QuantUniversity LLC. Hosted By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 02/10/2021 Qu.Academy https://quspeakerseries22.splashthat.com/
- 2. 2 QuantUniversity • Boston-based Data Science, Quant Finance and Machine Learning training and consulting advisory • Trained more than 1000 students in Quantitative methods, Data Science and Big Data Technologies using MATLAB, Python and R • Building a platform for AI and Machine Learning Exploration and Experimentation
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