Power Management in Smart Residential Building with Deep Learning Model for Occupancy Detection by Usage Pattern of Electric Appliances

With the growth of smart building applications, occupancy information in residential buildings is becoming more and more significant. In the context of the smart buildings' paradigm, this kind of information is required for a wide range of purposes, including enhancing energy efficiency and occupant comfort. In this study, occupancy detection in residential building is implemented using deep learning based on technical information of electric appliances. To this end, a novel approach of occupancy detection for smart residential building system is proposed. The dataset of electric appliances, sensors, light, and HVAC, which is measured by smart metering system and is collected from 50 households, is used for simulations. To classify the occupancy among datasets, the support vector machine and autoencoder algorithm are used. Confusion matrix is utilized for accuracy, precision, recall, and F1 to demonstrate the comparative performance of the proposed method in occupancy detection. The proposed algorithm achieves occupancy detection using technical information of electric appliances by 95.7~98.4%. To validate occupancy detection data, principal component analysis and the t-distributed stochastic neighbor embedding (t-SNE) algorithm are employed. Power consumption with renewable energy system is reduced to 11.1~13.1% in smart buildings by using occupancy detection.

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.

Learning Dense Features for Point Cloud Registration Using Graph Attention Network

Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. The successful registration relies on extracting robust and discriminative geometric features. Existing learning-based methods require high computing capacity for processing a large number of raw points at the same time. Although these approaches achieve convincing results, they are difficult to apply in real-world situations due to high computational costs. In this paper, we introduce a framework that efficiently and economically extracts dense features using graph attention network for point cloud matching and registration (DFGAT). The detector of the DFGAT is responsible for finding highly reliable key points in large raw data sets. The descriptor of the DFGAT takes these key points combined with their neighbors to extract invariant density features in preparation for the matching. The graph attention network uses the attention mechanism that enriches the relationships between point clouds. Finally, we consider this as an optimal transport problem and use the Sinkhorn algorithm to find positive and negative matches. We perform thorough tests on the KITTI dataset and evaluate the effectiveness of this approach. The results show that this method with the efficiently compact keypoint selection and description can achieve the best performance matching metrics and reach highest success ratio of 99.88% registration in comparison with other state-of-the-art approaches.

Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder

Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical task to enhance reliability, stability, and efficiency of the power system. This paper presents an anomaly detection process to find outliers observed in the smart metering system. In the proposed approach, bi-directional long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous data point. It calculates the reconstruction error through autoencoder with the non-anomalous data, and the outliers to be classified as anomalies are separated from the non-anomalous data by predefined threshold. Anomaly detection method based on the BiLSTM autoencoder is tested with the metering data corresponding to 4 types of energy sources electricity/water/heating/hot water collected from 985 households.