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AI data pipeline improves U.S. building meter forecasts

By PulseAugur Editorial · [1 sources] ·

A new research paper details a data-onboarding pipeline designed to detect and repair defects in U.S. building meter data before it's used to train AI demand forecasting models. The pipeline aims to improve forecast accuracy by addressing issues like missing readings or sensor freezes. Controlled experiments on the Building Data Genome 2 dataset demonstrated that the pipeline effectively restored forecast accuracy to baseline levels, even at defect prevalences observed in field studies, while retaining most of the training targets. AI

IMPACT Enhances the reliability of AI models used in critical infrastructure forecasting.

RANK_REASON The cluster contains a research paper detailing a new method for data onboarding in AI forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI data pipeline improves U.S. building meter forecasts

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The cluster contains a research paper detailing a new method for data onboarding in AI forecasting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yixuan Liang ·

    Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result

    arXiv:2610.02397v1 Announce Type: new Abstract: Electric utilities and grid operators increasingly rely on machine-learning models to forecast next-day demand, and those models learn from meter data that is routinely defective: readings go missing, sensors freeze, buildings read …

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