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A voltmeter is an electrical device that measures the potential difference or voltage between two points. It is connected in parallel with the circuit element it is measuring. A parallel connection is used because elements in parallel experience the same potential difference. The voltmeter is represented by the symbol "V ".
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Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
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An ammeter is a current measuring instrument. In the circuit, it is represented by the symbol A. The ammeter is placed in series with the device or component to measure the current. A series connection is used because objects in series have the same current passing through them. If a circuit has multiple resistors and the current needs to be measured in each resistor, the number of ammeters required depends on whether the circuit is in series or parallel.
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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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The 'SmartNIALMeter' electrical appliance disaggregation dataset.

Manuel Vogel1, Martin Friedli1, Martin Camenzind1

  • 1Lucerne University of Applied Sciences and Arts, Engineering and Architecture, iHomeLab, Horw, 6048, Switzerland.

Data in Brief
|September 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a large dataset for electrical disaggregation (NILM) to improve appliance recognition from aggregated energy signals. The data supports developing new NILM algorithms for energy management and grid stability.

Keywords:
Electric loadLoad disaggregationNILM datasetsNon-intrusive load monitoring (NILM)Power signature

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Energy Systems

Background:

  • Non-intrusive load monitoring (NILM) identifies individual appliance energy use from aggregate signals.
  • NILM applications include user energy feedback and grid load shifting for distribution system operators (DSOs).

Purpose of the Study:

  • To present the SmartNIALMeter dataset, a comprehensive collection for NILM research.
  • To facilitate the development and validation of advanced NILM algorithms.

Main Methods:

  • Collected a large dataset from 20 buildings over two years, sampling at 5-second intervals.
  • Instrumented 100 appliances with dedicated sensors for sub-metering, alongside smart meter data for total consumption.
  • Included diverse loads like heat pumps and EV charging stations, and both single-phase and three-phase devices.

Main Results:

  • The dataset comprises detailed energy consumption data for numerous appliances across various building types.
  • It captures real-world load variability, essential for robust NILM algorithm training and testing.
  • Provides ground truth for appliance-specific energy usage.

Conclusions:

  • The SmartNIALMeter dataset is a valuable resource for advancing NILM research.
  • Enables investigation into NILM algorithms, particularly those requiring limited sub-metered data.
  • Supports applications in energy efficiency and smart grid management.