Q. How does the predictability of photovoltaic and wind energy production affect load balancing?

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Q. What are some potential options for reducing data volumes from electricity meters in the smart grid?

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Q. What does event-driven metering (EDM) focus on to reduce data volumes in metering?

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Q. In the context of EDM, what defines an event?

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Q. How does the EDM method handle energy variations in metering data?

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Q. What are the conditions for an event to be detected in the EDM method?

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Q. How does EDM change the timing of data transmission compared to frequent TDM?

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Q. What is the primary advantage of EDM over TDM in terms of data volumes?

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Q. How is data transmission handled in EDM regarding polling requests from the grid side?

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Q. What is one of the main advantages of EDM over TDM in terms of information flow?

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Q. How does the time-wrapping/compression property of EDM impact data analysis?

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Q. What is the main advantage of EDM over TDM in terms of data exchange?

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Q. What is the key benefit of using low-cost electronic meters for tracking real-life processes in energy terms?

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

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Q. What is the role of cooperative metering schemes in the smart grid?

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Q. How can variations in the grid be controlled using the Ej(tk) values?

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Q. What is the key advantage of using process knowledge to reduce data volumes?

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Q. What is a characteristic of nomothetic processes?

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Q. What is an example of variability introduced by nomothetic processes in photovoltaic generation?

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Q. How can knowledge about ideal PV production shapes and topology help reduce data volumes?

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