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Welcome to the Application of Big Data in Analyzing Electric Meter Data MCQs Page

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 10 of 12

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Q91.
How can the compression ratio offered by EDM be adjusted?

a.

By changing the metering time intervals.

b.

By varying the communication standards.

c.

By adjusting the cost of data exchange.

d.

By varying the fuzzy thresholds (ฮดE).

Discuss
Answer: (d).By varying the fuzzy thresholds (ฮดE). Explanation:The compression ratio offered by EDM can be adjusted by varying the fuzzy thresholds (ฮดE) that distinguish between "meaningful" and "irrelevant" energy dynamics.
Q92.
What is the role of cooperative metering schemes in the smart grid?

a.

They reprogram meters for advanced data analysis.

b.

They focus on individual energy efficiency objectives.

c.

They involve listeners processing network messages.

d.

They primarily use Phasor techniques for control.

Discuss
Answer: (c).They involve listeners processing network messages. Explanation:Cooperative metering schemes involve listeners receiving and processing network messages.
Q93.
How can variations in the grid be controlled using the Ej(tk) values?

a.

By summing the variations within subtopologies.

b.

By using Phasor techniques.

c.

By adjusting the fuzzy thresholds.

d.

By changing the metering time intervals.

Discuss
Answer: (a).By summing the variations within subtopologies. Explanation:Variations in the grid can be controlled by summing the variations occurring within subtopologies using the Ej(tk) values.
Q94.
What is the key advantage of using process knowledge to reduce data volumes?

a.

It maximizes individual energy efficiency objectives.

b.

It allows for real-time facts sharing between PV plants.

c.

It enables anticipatory control to optimize energy flows.

d.

It introduces more variability into the data.

Discuss
Answer: (c).It enables anticipatory control to optimize energy flows. Explanation:Process knowledge can enable anticipatory control to optimize energy flows.
Q95.
What is a characteristic of nomothetic processes?

a.

They have irregular time series of data.

b.

They are affected by unknown agents.

c.

They show repetitive time series of data.

d.

They are primarily used for photovoltaic generation.

Discuss
Answer: (c).They show repetitive time series of data. Explanation:Nomothetic processes show repetitive time series of data.
Q96.
What is an example of variability introduced by nomothetic processes in photovoltaic generation?

a.

Moving clouds affecting daily Sun path.

b.

Local information related to production dynamics.

c.

Cooperative metering schemes.

d.

Real-time facts sharing between PV plants.

Discuss
Answer: (a).Moving clouds affecting daily Sun path. Explanation:Variability introduced by nomothetic processes in photovoltaic generation includes moving clouds affecting daily Sun path.
Q97.
How can knowledge about ideal PV production shapes and topology help reduce data volumes?

a.

By increasing the number of data samples.

b.

By formalizing the Sun path calculations.

c.

By calculating PV production for the next day.

d.

By finding and expressing dependencies between data series.

Discuss
Answer: (d).By finding and expressing dependencies between data series. Explanation:Knowledge about ideal PV production shapes and the topology can help reduce data volumes by finding and expressing dependencies between data series.
Q98.
What is one way in which the Earth's rotation affects PV energy production?

a.

It causes simultaneous sunrise and sunset events in different places.

b.

It results in different locations having the same energy variations.

c.

It leads to varying energy variations in different PV plants.

d.

It influences the speed of electronic communication.

Discuss
Answer: (a).It causes simultaneous sunrise and sunset events in different places. Explanation:The Earth's rotation causes non-simultaneous sunrise and sunset events in different places.
Q99.
How can the knowledge about time translation of effects help reduce data volumes?

a.

By increasing the speed of electronic communication.

b.

By eliminating the need for formalized knowledge.

c.

By decreasing the number of observers in the neighborhood.

d.

By calculating the effects caused by the same agent in terms of energy dynamics.

Discuss
Answer: (d).By calculating the effects caused by the same agent in terms of energy dynamics. Explanation:Knowledge about time translation of effects can help reduce data volumes by calculating the effects caused by the same agent in terms of energy dynamics.
Q100.
What are some factors that influence the renewable energy output of PV plants?

a.

Geographical coordinates, installation parameters, and weather conditions

b.

Installation costs, government regulations, and consumer demand

c.

Grid topology, transformer capacity, and voltage stability

d.

Legacy systems, ERP software, and central databases

Discuss
Answer: (a).Geographical coordinates, installation parameters, and weather conditions Explanation:The renewable energy output of PV plants is influenced by factors such as geographical coordinates, installation parameters, weather conditions, and more.
Page 10 of 12

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