Peer-to-peer (P2P) energy trading system has the ability to completely revolutionize the current household energy system by sharing energy among residents. As the number of customers employing distributed energy resources (DERs) such as solar rooftops increase, innovation in the double auction market (DA) system is becoming more significant. In this paper, a novel model-based asynchronous advantage actor-centralized-critic with communication (MB-A3C3) approach is carried out. The model is conducted on a large scale real-world hourly 2012-2013 dataset of 300 households in Sydney having rooftop solar systems installed in New South Wales (NSW), Australia. Results reveal that the MB-A3C3 approach outperforms other reinforcement learning methods (MADDPG and A3C3), producing lower community energy bills for 300 households. In closing the gap between the real-world and theoretical problems, the algorithms herein aid in reducing customers' electricity bills.INDEX TERMS peer-to-peer energy trading, model-based reinforcement learning, multi-agent reinforcement learning, deep learning approach
All machines in power plants need high reliability and to be continuous run at all times in the production process. The Remaining Useful Life (RUL) prediction of machines is an estimation for planning maintenance activities in advance to save the cost of corrective and preventive maintenance. Most existing models analyze sensor data separately. This univariate analysis never considers the relationship between sensors and time simultaneously. In this paper, we applied a Convolutional Neural Network (CNN), which considered both dimensions of and sensors; a multivariate time series analysis. Furthermore, we applied many techniques to enhance the framework of deep learning, including dropout, L2 Regularization, and the Adaptive Gradient Descent (AdaGrad). For the experiment, we conducted our method and showed the performance in term of Root Mean Square Error (RMSE) on a standard benchmark and for real-case datasets.
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