A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING

ABSTARCT :

A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING

EXISTING SYSTEM :

? A hybrid cloud and edge framework is presented for the control of DR resources, including BESS and HVAC units. ? The problem that the training of the DRL-based method requires power computing resources can be solved. The proposed framework saves the high cost of local computing resources. With this framework commissioned, the learning in the cloud and the real-time control in the edge can be fully automated, and the DR resources can respond to the DR plan actively and timely with limited human participation

DISADVANTAGE :

? The optimization process is usually timeconsuming, especially when the dimension of the decision variables is very large. To solve the optimization problem efficiently, a powerful computing resource is required. However, the current buildings suffer from the high cost of computing resources and lack a cost-effective automation system, which becomes the main obstacle to the popularization of the DR program.

PROPOSED SYSTEM :

? The same finding can be concluded for the slight illegal action percentage in the first 500 episodes. The gap becomes smaller after 500 episodes. It shows the significance of transfer learning in online training. From the perspective of average daily cost, it can be concluded in Table III that both training a new model ($12.1) and transferring from the existing model ($11.7) are very close to the optimization result ($11.1). But directly using the existing model ($18.2) will cause a 63.96% higher cost than the optimal solution.

ADVANTAGE :

? Luo at el. pointed out that cloud-based information infrastructure will be widely used in the next-generation power grid. Customer-oriented energy management as a service (EMaaS) under the cloud framework is put forward . The cloud can provide energy management service through solving the optimization for various types of load, such as electric vehicles (EVs) and air-conditioners . With the aid of the cloud, end-users only need to pay-on-demand and largely reduce the local investment cost and operation cost on local hardware

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