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AI model forecasts power outages 2 hours ahead with 2.4x better accuracy

By PulseAugur Editorial · [1 sources] ·

A machine learning model has been developed to forecast new power outages at an hourly resolution for 216 counties, predicting events up to two hours in advance. This model, an XGBoost implementation, utilizes storm-related data from HRRR forecasts, county-specific information, and current outage data. It significantly outperforms existing National Weather Service and SPC outlooks, achieving 2.4 times the PR-AUC and a higher precision at equivalent alert volumes. AI

IMPACT This model could improve disaster preparedness and utility response by providing more accurate and timely power outage predictions.

RANK_REASON The cluster describes a novel application of machine learning for forecasting a real-world phenomenon, supported by a paper and code release. [lever_c_demoted from research: ic=1 ai=1.0]

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

AI model forecasts power outages 2 hours ahead with 2.4x better accuracy

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5 / 100
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The cluster describes a novel application of machine learning for forecasting a real-world phenomenon, supported by a paper and code release. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/CarRepresentative843 ·

    Forecasting new power outages per county, hourly, 2 h ahead: 2.4× the PR-AUC of NWS warnings + SPC outlook on a held-out year [P]

    <!-- SC_OFF --><div class="md"><p>Hi all! I built an hourly <strong>power</strong> <strong>outage</strong> <strong>forecast</strong> for 216 counties (MO + IL): P(≥5% of a county's customers newly out in the 6 h starting 2 h from now). Model: XGBoost on HRRR storm-related data (g…

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