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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 11 of 12

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Q101.
Why is predictability important for renewable energy sources like PV plants?

a.

To reduce the cost of energy reserves

b.

To increase system stability

c.

To maximize the energy generation capacity

d.

To minimize the degradation of PV panels

Discuss
Answer: (b).To increase system stability Explanation:Predictability is important for renewable energy sources like PV plants to increase system stability.
Q102.
What challenges arise when dealing with independent forecasts for PV plants?

a.

They lead to synchronized energy generation.

b.

They make grid management easier.

c.

They limit the massive RES generation.

d.

They eliminate the need for energy reserve.

Discuss
Answer: (c).They limit the massive RES generation. Explanation:Independent forecasts for PV plants limit the massive renewable energy source (RES) generation, and this can be challenging in some cases.
Q103.
What is the purpose of using cloud index in the context of PV energy production?

a.

To estimate the geographical distribution of PV plants

b.

To partition the Event Space into numbered similarity clusters

c.

To calculate the ideal load shape for PV plants

d.

To measure the degradation of PV panels

Discuss
Answer: (b).To partition the Event Space into numbered similarity clusters Explanation:Cloud index is used to partition the Event Space into numbered similarity clusters, representing different meteorological conditions.
Q104.
How does the proposed method reduce data volumes and data rates when dealing with PV power dropouts?

a.

By sending a data packet into the network for every change in PV output

b.

By setting a high ฮ”E threshold for expected values

c.

By filtering out expected values and sending messages only for unexpected significant power dropouts

d.

By using a fixed ฮ”E threshold for all PV plants

Discuss
Answer: (c).By filtering out expected values and sending messages only for unexpected significant power dropouts Explanation:The proposed method reduces data volumes and data rates by filtering out expected values and sending messages only for unexpected significant power dropouts.
Q105.
Why does the calculation of effects caused by clouds at one PV site become statistically relevant to neighbors?

a.

Because clouds are responsible for diverse changes in PV output

b.

Because it is important to share weather forecasts among neighbors

c.

Because neighboring PV plants experience similar effects caused by clouds

d.

Because the energy dynamics in PV plants depend on their geographical location

Discuss
Answer: (c).Because neighboring PV plants experience similar effects caused by clouds Explanation:The calculation of effects caused by clouds at one PV site becomes statistically relevant to neighbors because some of them will experience effects of the same cause being translated.
Q106.
How can pattern-matching techniques be used in the context of PV energy production optimization?

a.

To predict the size of PV systems

b.

To estimate the speed of passing clouds

c.

To discover repetitive patterns in time series from different PV nodes

d.

To analyze the impact of atmospheric disturbances on PV output

Discuss
Answer: (c).To discover repetitive patterns in time series from different PV nodes Explanation:Pattern-matching techniques can be used to discover repetitive patterns in time series coming from different PV nodes.
Q107.
How does temporal distance vary between observers in different geographical locations, especially in terms of the impact of Earth's rotation?

a.

Temporal distance is constant between all observers.

b.

Observers in the east detect effects earlier than those in the west.

c.

Temporal distance increases with increasing geographical distance.

d.

Temporal distance is not related to geographical coordinates.

Discuss
Answer: (b).Observers in the east detect effects earlier than those in the west. Explanation:Observers in the east detect effects earlier than those in the west due to the impact of Earth's rotation.
Q108.
What is the role of pattern matching in the anticipatory control of PV energy production?

a.

To determine the geographical coordinates of PV plants

b.

To compare absolute PV energy dynamics between plants

c.

To validate the relationships between PV energy dynamics

d.

To forecast the impact of cloud variability on PV output

Discuss
Answer: (c).To validate the relationships between PV energy dynamics Explanation:Pattern matching is used to validate the relationships between PV energy dynamics, confirming the transitivity of cause and effect relationships between different nodes.
Q109.
What is the primary purpose of the EDM method in smart grids?

a.

To maximize data volumes

b.

To capture energy dynamics in real time

c.

To reduce the impact of cloud variability

d.

To forecast the impact of natural phenomena

Discuss
Answer: (b).To capture energy dynamics in real time Explanation:The EDM method captures energy dynamics in real time, reducing the need for frequent time-driven observations.
Q110.
How does the EDM method help reduce data volumes in smart grids?

a.

By encouraging very frequent time-driven observations

b.

By increasing the amount of redundant data

c.

By supplying only the unexpected energy dynamics

d.

By focusing on long-term cooperative approaches

Discuss
Answer: (c).By supplying only the unexpected energy dynamics Explanation:The EDM method reduces data volumes by supplying only the unexpected energy dynamics, eliminating the need for redundant data.
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