相关论文: Avoiding Occupancy Detection from Smart Meter usin…
In recent years, many proposals have arisen from research on privacy in smart metering. In one of the considered approaches, referred to as anonymization, smart meters transmit fine-grained electricity consumption values in such a way that…
The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications. These applications gather contextual information about users and provide some services, such as personalized offers, through…
The large scale deployment of Advanced Metering Infrastructure among residential energy customers has served as a boon for energy systems research relying on granular consumption data. Residential Demand Response aims to utilize the…
The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential electricity demand remains…
In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications usually is…
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity…
In order to keep track of the operational state of power grid, the world's largest sensor systems, smart grid, was built by deploying hundreds of millions of smart meters. Such system makes it possible to discover and make quick response to…
We present a new machine learning-based attack that exploits network patterns to detect the presence of smart IoT devices and running services in the WiFi radio spectrum. We perform an extensive measurement campaign of data collection, and…
The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a…
With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly. NILM estimates the power consumption of individual devices given their aggregate consumption. In this way, the…
Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior…
With the development of IoT technologies in the past few years, a wide range of smart devices are deployed in a variety of environments aiming to improve the quality of human life in a cost efficient way. Due to the increasingly serious…
In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML)…
The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. Smart meters enable a two way communication between residential customers and utilities.…
The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises…
The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption…
This thesis explores the extent to which passive observation of wireless traffic in a smart home environment can be used to infer privacy-invasive information about its inhabitants. Using a setup that mimics the capabilities of a nosy…
Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection…
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous…
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One example is Multivariate…