Artificial Intelligence and Machine Learning for Demand-Side Response
Presented by: Ioannis Antonopoulos, Benoit Couraud, and Valentin Robu
In the recent years, there has been a growing interest for the use of Distributed Demand-Side-Response (DDSR) to regulate the power system. DDSR consists in the coordination of distributed loads such as industrial, commercial and recently residential end-users to contribute to electricity suppliers’ portfolio balance or frequency regulation. The integration of commercial and residential end-users into DDSR comes with the need for Big Data Analysis and Artificial Intelligence (AI) solutions to optimize the contribution of these distributed assets. In this webinar, we describe what the key challenges of DDSR are, and how AI and Machine Learning (ML) solutions can address these challenges. Based on a recent review of research works and industrial projects, we will detail the principles of the most relevant AI techniques and will explain how they are used in the context of DDSR. Finally, we discuss a number of directions for future research in this area.
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INTENDED AUDIENCE:
- Engineers and Researchers in demand-side response and demand-side management
- Practitioners working in the power industry, such as network operators, regulators and in providing demand response and aggregation services
- Researchers working in data analytics, AI and machine learning for power systems, with an interest in demand response
- Innovative energy start-ups entering this fast-developing field
ABOUT THE SPEAKERS
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Tags & Topics for This Webinar:
Artificial Intelligence; Machine Learning; Artificial Neural Networks; Nature-Inspired Intelligence; Multi-agent Systems; Demand Response; Power Systems
For any questions, please contact Phyllis Caputo at p.caputo@ieee.org. To view previous webinars on-demand, visit the IEEE Smart Grid Resource Center |