UN SDG #7 Affordable and Clean Energy UN SDG #7
UN SDG #9 Industry, Innovation and Infrastructure UN SDG #9
challenge
0 shares
Future Energy Systems Management
High volumes of data are becoming available with the growth of the advanced metering infrastructure and massive deployment of IoT devices. These are expected to benefit the planning and operation of the future energy systems and to help the customers transition from a passive to an active role.
challenge
0 shares
Future Energy Systems Management
High volumes of data are becoming available with the growth of the advanced metering infrastructure and massive deployment of IoT devices. These are expected to benefit the planning and operation of the future energy systems and to help the customers transition from a passive to an active role.
the problem
Nature and Context help
A novel approach for future energy systems is using deep reinforcement learning, a hybrid approach that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure gets benefited from two methods, Deep Q-learning and Deep Policy Gradient. The hybrid approach is capable of handling multiple actions simultaneously.
Symptoms and Causes help
the impact
Negative Effects help
Economic Impact help
Success Metrics help
who benefits from solving this problem
Organization Types help
Stakeholders help
financial insights
Current Funding help
Potential Solution Funding help
ideas
Ideas Description help
Ideas Value Proposition help
Ideas Sustainability help
attributions
Data Sources help
Contributors to this Page help