Smart meter

Summary

A smart meter is an electronic device that records information—such as consumption of electric energy, voltage levels, current, and power factor—and communicates the information to the consumer and electricity suppliers. Such an advanced metering infrastructure (AMI) differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier. Description The term smart meter often refers to an electricity meter, but it also may mean a device measuring natural gas, water or district heating consumption. More generally, a smart meter is an electronic device that records information such as consumption of electric energy, voltage levels, current, and power factor. Smart meters communicate the information to the consumer for greater clarity of consumption behavior, and electricity suppliers for system monitoring and customer billing. Smart meters typically record energy near real-time, and report regularly, short intervals throughout

Official source

https://en.wikipedia.org/wiki/Smart_meter

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (31)

Related publications

Related people (4)

Related people

Related units (2)

Related units

Related concepts (18)

Related concepts

Related courses (8)

Related courses

Related lectures (8)

Related lectures

Three Essays on Methodologies for Dynamic Modeling of Emerging Socio-technical Systems

Mohammad Amin Dehdarian

Socio-technical energy transitions are long-term and major transformations in incumbent energy infrastructures. They include fundamental changes in technologies as well as institutions and social patterns. Transition studies are primarily focused on frameworks for analyzing the entire transition process by investigating the historical cases of transitions. A multi-phase approach to transition posits this process begins with a pre-development phase characterized by technological and institutional lock-ins, and resistance from incumbent actors. This period is critical for a forward-looking approach to transitions, since early developments shape path-dependent and irreversible processes leading to the emergence of new transition pathways. However, our understanding about the mechanisms and dynamics of this phase is still very limited. This is mainly due to lack of data, weak conceptualization and the necessity of developing new methods proper to deal with these limitations. This dissertation develops methodologies for investigating some complex questions arising in the pre-development phase, by focusing on the case of smart grid development. The first essay uses insights from modeling interventions in complex systems and builds a System Dynamics model to investigate the cost allocation problem of smart metering roll-out. The second essay takes ideas from Technological Innovation System approach and develops a method to analyze the emergence of spatial diversity in smart grid development by combining Social Network Analysis and Agent-Based Modeling. The third essay builds on ideas from network theory and evolutionary modeling to develop a method for identifying the main path of knowledge development and analyzing knowledge trajectories in smart grid initiatives.

EPFL2017

, , ,

The ability of smart meters to communicate energy consumption data in (near) real-time enables data analytics for novel applications, such as pervasive demand response, personalized energy feedback, outage management, and theft detection. Smart meter data are characterized by big volume and big velocity, which make processing and analysis very challenging from a computational point of view. In this paper we presented SmartD, a dashboard that enables the data analyst to visualize smart meter data and estimate the typical load profile of new consumers according to different contexts, temporal aggregations and consumer segments.

2014

iDR: Consumer and Grid Friendly Demand Response System

Tri Kurniawan Wijaya

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data. In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers’ house-level consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider’s DR objectives - such as reduction in peak load - while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR’s capabilities are demonstrated using real-world house-level as well as appliance-level data.

2014