相关论文: A Privacy-Preserving Energy Theft Detection Model …
Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning technique for identifying energy theft…
Energy theft poses a significant threat to the stability and efficiency of smart grids, leading to substantial economic losses and operational challenges. Traditional centralized machine learning approaches for theft detection require…
The roll-out of smart meters in electricity networks introduces risks for consumer privacy due to increased measurement frequency and granularity. Through various Non-Intrusive Load Monitoring techniques, consumer behavior may be inferred…
Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a…
Frequent metering of electricity consumption is crucial for demand side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer…
Most of the smart applications, such as smart energy metering devices, demand strong privacy preservation to strengthen data privacy. However, it is difficult to protect the privacy of the smart device data, especially on the client side.…
In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the…
Smart meters are key elements for the operation of smart grids. By providing near realtime information on the energy consumption of individual users, smart meters increase the efficiency in generation, distribution and storage of energy in…
In the electricity grid, networked sensors which record and transmit increasingly high-granularity data are being deployed. In such a setting, privacy concerns are a natural consideration. We present an attack model for privacy breaches,…
Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and householdes) via solar panels, wind turbines or local generators in the past decade. With local energy, such…
In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a…
With the increasing integration of smart meters in electrical grids worldwide, detecting energy theft has become a critical and ongoing challenge. Artificial intelligence (AI)-based models have demonstrated strong performance in identifying…
The next-generation energy network, the so-called smart grid (SG), promises a tremendous increase in efficiency, safety and flexibility of managing the electricity grid as compared to the legacy energy network. This is needed today more…
High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer's life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models…
Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information.…
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…
Smart grids leverage data from smart meters to improve operations management and to achieve cost reductions. The fine-grained meter data also enable pricing schemes that simultaneously benefit electricity retailers and users. Our goal is to…
The increasing adoption of advanced metering infrastructure has led to growing concerns regarding privacy risks stemming from the high resolution measurements. This has given rise to privacy protection techniques that physically alter the…
In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, billing, etc.…
Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage…