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This paper identifies an Accuracy-Efficiency Paradox in on-device energy forecasting where high-precision models can cause net energy loss due to inference energy consumption and battery aging, and proposes a Total Cost of Ownership framework to minimize this loss.
This paper introduces BatteryLake, a governed data lakehouse that uses LLM agents for evidence-grounded metadata extraction and schema mapping, with human-in-the-loop verification, to curate heterogeneous battery aging datasets and release an open benchmark.