INRIA（法国国家信息与自动化研究所）；Ecole Polytechnique（巴黎综合理工学院）；Technische Universität Wien（维也纳工业大学）；CentraleSupélec（中央理工-高等电力学院）；CNRS（法国国家科学研究中心）；Universite Paris-Saclay（巴黎萨克雷大学）
Ganesh Del Grosso, Georg Pichler, Pablo Piantanida
Power consumption data is very useful as it allows to optimize power grids, detect anomalies and prevent failures, on top of being useful for diverse research purposes. However, the use of power consumption data raises significant privacy concerns, as this data usually belongs to clients of a power company. As a solution, we propose a method to generate synthetic power consumption samples that faithfully imitate the originals, but are detached from the clients and their identities. Our method is based on Generative Adversarial Networks (GANs). Our contribution is twofold. First, we focus on the quality of the generated data, which is not a trivial task as no standard evaluation methods are available. Then, we study the privacy guarantees provided to members of the training set of our neural network. As a minimum requirement for privacy, we demand our neural network to be robust to membership inference attacks, as these provide a gateway for further attacks in addition to presenting a privacy threat on their own. We find that there is a compromise to be made between the privacy and the performance provided by the algorithm.
University of Hertfordshire（赫特福德大学）；Leeds Beckett University（利兹贝克特大学）
Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task smart meter have been used for load forecasting, reduction of consumer bills as well as reduction of grid distortions. Except energy consumption smart meters can be used to disaggregate energy consumption on device level. In this paper we investigate the potential of identifying the multimedia content played by a TV or monitor device using the central house's smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed arc…
New Mexico Institute of Mining and Technology（新墨西哥矿业与技术学院）
Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important feat…
Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recently several studies have proposed a new approach based on multi label classification. Different appliances are treated as separate classes, and the task is to identify the classes given the aggregate smart-meter reading. Prior studies in this area have used off the shelf algorithms like MLKNN and RAKEL to address this problem. In this work, we propose a deep learning based technique. There are hardly any studies in de…
Northeast Normal University（东北师范大学）；Changchun University of Technology（长春工业大学）；University of Missouri（密苏里大学）；Qinghu Rising Sunshine Data Technology (Beijing) Co., Ltd（清湖旭日阳光数据技术（北京）有限公司）；National Institute Ltd（国家研究院有限公司）
Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. From the significant difference between the predicted trajectory and the observed one, the meters that cannot measure electricity accurately are located. In a case study, a proof of principle was demonstrated in detecting inaccurate meters with high accuracy for practical usage to…
Technical University of Denmark（丹麦技术大学）；Niras（尼拉斯公司）
In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex tempor…
In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart…
Nowadays IoT based applications are becoming more popular because it provides efficient solutions for many real time problems. In this paper, an IoT based electric meter monitoring system using android application has been proposed that aims to reduce manual efforts for measuring the electricity units and make users concern about the excessive usage of electricity. Aurdino Uno and an optical sensor are used to fetch the pulse of the electric meter. In order to reduce human error and cost in energy consumption, a low cost wireless sensor network is implemented for digital energy meter and a mob…
As the cost of the residential solar system decreases, rooftop photovoltaic (PV) has been widely integrated into distribution systems. Most rooftop PV systems are installed behind-the-meter (BTM), i.e., only the net demand is metered, while the native demand and PV generation are not separately recorded. Under this condition, the PV generation and native demand are invisible to utilities, which brings challenges for optimal distribution system operation and expansion. In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from…
Privacy-preserving smart meter control strategies proposed in the literature so far make some ideal assumptions such as instantaneous control without delay, lossless energy storage systems etc. In this paper, we present a one-step-ahead predictive control strategy using Bayesian risk to measure and control privacy leakage with an energy storage system. The controller estimates energy state using a three-circuit energy storage model to account for steady-state energy losses. With numerical experiments, the controller is evaluated with real household consumption data using a state-of-the-art adv…
Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-lea…
Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between …
Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to …
In the present scenario rate of utilization of electrical energy is continuously increasing, to preserve the non-renewable energy resources, efficient consumption of electrical energy is one of the most important needs of this era. Smart Energy Meter (SEM) plays a vital role in efficient use of electrical energy in consumer premises. Accuracy in the measurement of current is one of the key factors to be considered while measuring energy consumption. This paper introduces Arduino based low cost single-phase SEM and proposes a linear regression based technique for accurate measurement of current…
Electricity is an essential comfort to support our daily activities. With the competitive increase and energy costs by the industry, new values and opportunities for delivering electricity to customers are produced. One of these new opportunities is electric vehicles. With the arrival of electric vehicles, various challenges and opportunities are being presented in the electric power system worldwide. For example, under the traditional electric power billing scheme, electric power has to be consumed where it is needed so that end-users could not charge their electric vehicles at different poin…
We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. We use this fundamental bound to develop privacy-preserving policies. Particularly, we devise a load-scheduling policy by maximizing the lower bound on the expected estimation error of non-intrusive load monitoring algorithms.
The knowledge of distribution grid models, including topologies and line impedances, is essential to grid monitoring, control and protection. However, this information is often unavailable, incomplete or outdated. The increasing deployment of smart meters (SMs) provides a unique opportunity to address this issue. This paper proposes a two-stage data-driven framework for distribution grid modeling using SM data. In the first stage, we propose to identify the topology via reconstructing a weighted Laplacian matrix of distribution networks, which is mathematically proven to be robust against mode…
Stanford University（斯坦福大学）；Arizona State University（亚利桑那州立大学）
The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' last gasp signals, will have poor performance, because the renewable generators and storages and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the sto…
Energy meter measures the amount of power consumed by electrical loads in residential, industrial and commercial applications. In this project, the focus goes to the implementation of a smart power measurement system to allocate identification for individuals and determine the clients energy usage. The incorporation of two PIC 16F877A microcontrollers and radio-frequency identification (RFID) reader in this research work make the system operation smooth and reliable. This paper presents the development of an intelligent prepaid power metering system enabling power utilities to collect electric…
Smart meters have currently attracted attention because of their high efficiency and throughput performance. They transmit a massive volume of continuously collected waveform readings (e.g. monitoring). Although many compression models are proposed, the unexpected size of these compressed streams required endless storage and management space which poses a unique challenge. Therefore, this paper explores the question of can the compressed smart meter readings be re-compressed? We first investigate the applicability of re-applying general compression algorithms directly on compressed streams. Th…
The rapid development and expansion of the Internet of Things (IoT) paradigm has drastically increased the collection and exchange of data between sensors and systems, a phenomenon that raises serious privacy concerns in some domains. In particular, Smart Meters (SMs) share fine-grained electricity consumption of households with utility providers that can potentially violate users' privacy as sensitive information is leaked through the data. In order to enhance privacy, the electricity consumers can exploit the availability of physical resources such as a rechargeable battery (RB) to shape the…
Norwegian University of Science and Technology（挪威科技大学）；KWR Water Cycle Research Institute（KWR水循环研究所）；University of Cincinnati（辛辛那提大学）；Leiden University（莱顿大学）；Delft University of Technology（代尔夫特理工大学）
Socio-economic characteristics are influencing the temporal and spatial variability of water demand - the biggest source of uncertainties within water distribution system modeling. Improving our knowledge on these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socio-economic user characteristics by applying a novel clustering algorithm that uses dynamic time warping on daily demand patterns. The approach is tested on simulated and measured single family home datasets. We show that the novel algorithm performs better compared to co…
Fine-grained Smart Meters (SMs) data recording and communication has enabled several features of Smart Grids (SGs) such as power quality monitoring, load forecasting, fault detection, and so on. In addition, it has benefited the users by giving them more control over their electricity consumption. However, it is well-known that it also discloses sensitive information about the users, i.e., an attacker can infer users' private information by analyzing the SMs data. In this study, we propose a privacy-preserving approach based on non-uniform down-sampling of SMs data. We formulate this as the pr…
In this paper, we present a novel data-driven approach to detect outage events in partially observable distribution systems by capturing the changes in smart meters' (SMs) data distribution. To achieve this, first, a breadth-first search (BFS)-based mechanism is proposed to decompose the network into a set of zones that maximize outage location information in partially observable systems. Then, using SM data in each zone, a generative adversarial network (GAN) is designed to implicitly extract the temporal-spatial behavior in normal conditions in an unsupervised fashion. After training, an ano…
In the residential sector, electric water heaters are appliances with a relatively high power consumption and a significant thermal inertia, which is particularly suitable for Demand Response schemes. The success of efficient DR schemes via the control of water heaters presupposes an accurate estimate of their power demand at each instant. Although the load of water heaters is rarely directly measured, a large penetration of Smart Meters (SMs) in distribution grids enables to indirectly infer this information on a large scale via load disaggregation. For that purpose, a considerable number of …
McGill University（麦吉尔大学）；CentraleSupélec（中央理工-高等电力学院）；CNRS（法国国家科学研究中心）；Université Paris Sud（巴黎第十一大学）；Montreal Institute for Learning Algorithms (Mila)（蒙特利尔学习算法研究所）；Université de Montréal（蒙特利尔大学）
Smart Meters (SMs) are able to share the power consumption of users with utility providers almost in real-time. These fine-grained signals carry sensitive information about users, which has raised serious concerns from the privacy viewpoint. In this paper, we focus on real-time privacy threats, i.e., potential attackers that try to infer sensitive information from SMs data in an online fashion. We adopt an information-theoretic privacy measure and show that it effectively limits the performance of any attacker. Then, we propose a general formulation to design a privatization mechanism that can…
This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improv…
