Q. Why does the calculation of effects caused by clouds at one PV site become statistically relevant to neighbors?

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Q. How does the proposed method reduce data volumes and data rates when dealing with PV power dropouts?

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Q. What is the purpose of using cloud index in the context of PV energy production?

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Q. What challenges arise when dealing with independent forecasts for PV plants?

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

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Q. What are some factors that influence the renewable energy output of PV plants?

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Q. How can the knowledge about time translation of effects help reduce data volumes?

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Q. What is one way in which the Earth's rotation affects PV energy production?

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

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

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Q. What is a characteristic of nomothetic processes?

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

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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 role of cooperative metering schemes in the smart grid?

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

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

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

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

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