Electricity meter

About: Electricity meter is a research topic. Over the lifetime, 7894 publications have been published within this topic receiving 42430 citations. The topic is also known as: electric meter & electrical meter.

Utility electric meter web server

Abstract: A meter (M) installed by a utility at a customer site to measure the usage of a commodity supplied by the utility to the customer comprises an enclosure (E) in which is installed measuring apparatus (A) for measuring the amount of usage of the commodity by the customer at any one time. A web server (S) provides information obtained from the measuring apparatus and the utility to the customer. A wireless connection (C) between the web server and a customer device (D) allows the information and utility provided information to be displayed to the customer as well as enabling the device to obtain information from the meter so to determine when, and for how long, the device can be most cost efficiently operated.

PowerPlay: creating virtual power meters through online load tracking

Abstract: Online load tracking is the problem of monitoring an individual electrical load's energy usage by analyzing a building's smart meter data. The problem is important, since many energy optimizations require fine-grained, per-load energy data in real time; it also differs from the well-studied problem of load disaggregation in that it emphasizes efficient, online operation and per-load accuracy, rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to it. To enable high performance, we take a model-driven approach that focuses on efficiently detecting a small number of identifiable load features in smart meter data. Our results demonstrate that our system, called PowerPlay, i) enables efficient online tracking on low-power embedded platforms, ii) scales to thousands of loads (across many buildings) on server platforms, and iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm.

Selective wave guide energy meter

Abstract: My invention relates to a novel device for the independent measurement of the electrical components of a micro-wave signal, and more particularly to a novel measuring system for independently measuring incident and reflected waves. Wave guides are commonly used in micro-wave systems as a means...

Online power estimation of graphics processing units

Abstract: Accurate power estimation at runtime is essential for the efficient functioning of a power management system. While years of research have yielded accurate power models for the online prediction of instantaneous power for CPUs, such power models for graphics processing units (GPUs) are lacking. GPUs rely on low-resolution power meters that only nominally support basic power management. To address this, we propose an instantaneous power model, and in turn, a power estimator, that uses performance counters in a novel way so as to deliver accurate power estimation at runtime. Our power estimator runs on two real NVIDIA GPUs to show that accurate runtime estimation is possible without the need for the high-fidelity details that are assumed on simulation-based power models. To construct our power model, we first use correlation analysis to identify a concise set of performance counters that work well despite GPU device limitations. Next, we explore several statistical regression techniques and identify the best one. Then, to improve the prediction accuracy, we propose a novel application-dependent modeling technique, where the model is constructed online at runtime, based on the readings from a low-resolution, built-in GPU power meter. Our quantitative results show that a multi-linear model, which produces a mean absolute error of 6%, works the best in practice. An application-specific quadratic model reduces the error to nearly 1%. We show that this model can be constructed with low overhead and high accuracy at runtime. To the best of our knowledge, this is the first work attempting to model the instantaneous power of a real GPU system, earlier related work focused on average power.

Non-Intrusive Load Monitoring: Disaggregation of Energy by Unsupervised Power Consumption Clustering

Abstract: There is a growing trend in monitoring residential infrastructures to provide inhabitants with more information about their energy consumption and help them to reduce usage and cost. Device-level power consumption information, while a functionality in newer smart appliances, is not generally available to consumers. In electricity consumption disaggregation, Non-Intrusive Load Monitoring (NILM) refers to methods that provide consumers estimates of device-level energy consumption based on aggregate measurements usually taken at the main circuit panel or electric meter. The traditional NILM approach characterizes changes in the power signal when devices turn on or o , and it infers the consumption of different devices present in the home based on these changes. Generally, these NILM methods require training and models of the devices present in the home in order to function properly. Because of these challenges, much of the NILM literature does not address the actual energy disaggregation problem but focuses on detecting events and classifying changes in power. In this dissertation, we propose a relaxation to the traditional NILM problem and provide an unsupervised, data-driven algorithm to solve it. Specifically we propose Power Consumption Clustered Non-Intrusive Load Monitoring (PCC-NILM), a relaxation that reports on the energy usage of devices grouped together by power consumption levels. In order to solve the PCC-NILM problem, we provide the Approximate Power Trace Decomposition Algorithm (APTDA). Unlike other methods, APTDA does not require training and it provides estimated energy consumption for different classes of devices.