Q. How does the EDM method help reduce data volumes in smart grids?

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Q. What is the primary purpose of the EDM method in smart grids?

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Q. What is the role of pattern matching in the anticipatory control of PV energy production?

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Q. How does temporal distance vary between observers in different geographical locations, especially in terms of the impact of Earth's rotation?

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Q. How can pattern-matching techniques be used in the context of PV energy production optimization?

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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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