-Deep convolutional neural networks (CNNs) are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.
Due to an increase in computing power and innovative approaches of an end-to-end reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now plausible to combine RL and deep learning to perform smart building energy control (SBEC) systems. Deep reinforcement learning (DRL) revolutionizes the existing Q-learning algorithm to deep Q-learning (DQL) profited by artificial neural networks. Deep neural network (DNN) is well trained to calculate the Qfunction. To create a comprehensive SBEC system, it is crucial to choose an appropriate mathematical background and benchmark the best framework of a model-based predictive control to manage the building heating, ventilation, and air conditioning (HVAC) system. The main contribution of this paper is to explore a stateof-the-art DRL methodology to smart building control.
Keywords-Deep reinforcement learning, deep Q-learning, deep neural network, energy management system. ℒ( ) State, s(t) Action, a(t) Reward, r(t) Update w Current Q-values Q (s,a)
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