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Dive deep into the fascinating world of Application of Big Data in Analyzing Electric Meter Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Application of Big Data in Analyzing Electric Meter Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Application of Big Data in Analyzing Electric Meter Data, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Application of Big Data in Analyzing Electric Meter Data MCQs | Page 8 of 12

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Q71.
What is the main aim of load management in the context of electricity production and consumption?

a.

To maximize electricity production

b.

To minimize power outages

c.

To ensure the balance between consumption and production in real time

d.

To bill electricity consumers accurately

Discuss
Answer: (c).To ensure the balance between consumption and production in real time Explanation:The main aim of load management is to ensure the balance between consumption and production in real time.
Q72.
How does time-driven metering (TDM) differ from high-frequency sampling in electricity monitoring?

a.

TDM provides real-time data, while high-frequency sampling describes past events.

b.

TDM captures consumption dynamics caused by ironing processes.

c.

High-frequency sampling is used for billing purposes.

d.

TDM has a subsecond sampling frequency.

Discuss
Answer: (a).TDM provides real-time data, while high-frequency sampling describes past events. Explanation:TDM provides data that describe what had happened in the past, while high-frequency sampling captures real-time consumption dynamics.
Q73.
How frequently do meters in the smart grid communicate electricity quantities to the utility company?

a.

Hourly

b.

Every 15 minutes

c.

Every minute

d.

Every second

Discuss
Answer: (b).Every 15 minutes Explanation:Meters in the smart grid communicate electricity quantities every 15 minutes.
Q74.
What attributes are typically included in metering data packets?

a.

Voltage and current readings

b.

Timestamps, node identifications, and power information

c.

Security protocols and encryption

d.

Broadband communication details

Discuss
Answer: (b).Timestamps, node identifications, and power information Explanation:Metering data packets typically contain timestamps, node identifications, and power information.
Q75.
Why is the raw format of metering data rarely used in practice?

a.

It lacks timestamp information.

b.

It doesn't provide information about power and energy.

c.

Industrial protocols add security and interoperability features.

d.

Raw format is incompatible with smart meters.

Discuss
Answer: (c).Industrial protocols add security and interoperability features. Explanation:Industrial protocols are used to add security, interoperability, and other features to metering data.
Q76.
What is the challenge posed by operating subsecond sampling in electricity distribution grids?

a.

It generates too few data.

b.

It results in inaccuracies in metering data.

c.

It leads to data volumes expressed in petabytes and requires high data rates.

d.

It is incompatible with the known art of networking technologies.

Discuss
Answer: (c).It leads to data volumes expressed in petabytes and requires high data rates. Explanation:Subsecond sampling generates data volumes expressed in petabytes and requires high data rates, which may be challenging to manage.
Q77.
What is the primary role of smart metering in power systems using renewable energy sources (RES)?

a.

Controlling legacy energy plants

b.

Balancing energy production from variable RES

c.

Reducing electricity costs for consumers

d.

Enhancing the predictability of photovoltaic energy

Discuss
Answer: (b).Balancing energy production from variable RES Explanation:Smart metering plays a role in balancing energy production from variable RES by using observational data from within the grid.
Q78.
How does the predictability of photovoltaic and wind energy production affect load balancing?

a.

It simplifies load balancing.

b.

It has no impact on load balancing.

c.

It adds difficulty to load balancing.

d.

It reduces the need for load balancing.

Discuss
Answer: (c).It adds difficulty to load balancing. Explanation:The poor predictability of photovoltaic and wind energy production, which depends on changing weather conditions, adds difficulty to load balancing.
Q79.
What are some potential options for reducing data volumes from electricity meters in the smart grid?

a.

Locally calculating indicators to replace measurement data

b.

Replacing meters with more expensive equipment

c.

Increasing the sampling frequency of meters

d.

Using smart meters for non-real-time assessments

Discuss
Answer: (a).Locally calculating indicators to replace measurement data Explanation:One potential option is to use smart devices to locally calculate indicators to replace measurement data, reducing data volumes.
Q80.
What does event-driven metering (EDM) focus on to reduce data volumes in metering?

a.

Reducing the sampling frequency

b.

Eliminating the need for metering

c.

Concentrating on energy variations

d.

Increasing the data broadcast frequency

Discuss
Answer: (c).Concentrating on energy variations Explanation:Event-driven metering (EDM) concentrates on energy variations to reduce data volumes.
Page 8 of 12

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