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.
People Impacted
$ 111B
Potential Funding
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the problem
Nature and Context

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.

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