Search

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

Abstract This paper presents an approach to the power management of nanogrid clusters assisted by a novel form of peer‐to‐peer (P2P) electricity trading. DC nanogrids have lower power loss in real time and are suitable for P2P trading. Here, cost of electricity for clusters involving different kinds of photovoltaic (PV) power production (as a secondary source) is reduced by a newly proposed P2P trading scheme. For power management of individual clusters, multi‐objective optimization is applied to minimize simultaneously total power consumption, grid power consumption, and total delay incurred locally by scheduling. The temporal surplus of self‐supplied PV power of a cluster can be sold through P2P trading to another cluster subjected to a temporal power shortage. In P2P trading, a cooperative game model is used for buyers and sellers to maximize their welfare. To increase P2P trading efficiency, prediction of load demand and PV power production is considered for power management of each cluster to resolve instantaneous imbalances between load demand and PV power production. A gated recurrent unit network is used to forecast future load demand and PV power production in nanogrid cluster could reduce electricity cost by 29.2%.

Nanogrids can be considered smart grids that are implemented for small-scale buildings, houses, and apartments. A typical power management framework for nanogrids determines the scheduling of operations of electric appliances for each time interval with objectives related to total power consumption and total delay due to scheduling. Such a framework of power management has limitations in accommodating future operating conditions of nanogrids. Taking future outdoor temperature as a future operating condition, a proactive power management for nanogrids is presented in this paper. The goal of proactive power management for nanogrids is to achieve the proper level of indoor temperature in a cost-efficient way, sooner rather than later, by taking into account future outdoor temperature. To achieve this goal, a long short-term memory (LSTM) network is used as the controller. Simulations have been performed to verify the performance of the proposed power management. The results of the simulations demonstrate that living comfort measured in terms of room temperature is enhanced while the overall electricity cost is reduced, mainly due to the ability of the LSTM network to predict the trend of outdoor temperature.

Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning-based methods require high computing capacity for processing a large number of raw points at the same time, computational capacity limitation is not an issue thanks to powerful parallel computing process using GPU. In this paper, we introduce a framework that efficiently and economically extracts dense features using a 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 keypoints combined with their neighbors to extract invariant density features in preparation for the matching. The graph attention network (GAT) 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 the highest success ratio of 99.88% registration in comparison with other state-of-the-art approaches.

Wireless sensor networks (WSNs) have been widely used due to their extensive range of applications [...]

Intelligent Transportation System (ITS) has evolved into a system for provision of traffic information and traffic control with the help of advanced IT technologies [...]

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.