{
  "id": "AIBD-0168",
  "title": "NCAR's DICast wind forecasts cut Xcel Energy's forecast error to 10.8%; Xcel estimates US$49 million saved",
  "summary": "NCAR's adaptive forecast-blending system (DICast), used by Xcel Energy since 2009, cut mean wind forecast error from about 18% to 10.8% by 2014; Xcel estimated US$49m saved (IEEE Spectrum 2015).",
  "date_observed": "2015-10-22",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Sue Ellen Haupt and William Mahoney of the National Center for Atmospheric Research wrote in IEEE Spectrum (22 October 2015). NCAR's wind power forecasting system for Xcel Energy blends numerical weather models with the Dynamic Integrated foreCast (DICast) system, used for wind prediction since 2009. DICast constantly checks its predictions against outcomes and adjusts its bias corrections and weightings. The mean forecast error at a typical wind farm fell from about 18 percent in 2008 to 10.8 percent by 2014. Xcel estimated the improvements had saved customers US$49 million so far. A UCAR news item (7 February 2012) said the system saved ratepayers $6 million in 2010 alone. It described the work as building on NCAR research ranging from statistics to artificial intelligence. E&E News (4 December 2017) quoted Haupt that the forecasts could save $10 million a year. It reported that Xcel operators decommit large coal units when sustained wind is predicted. None of the sources describes the system as machine learning in so many words. The savings are Xcel's estimates reported through NCAR and the press, not a regulator's audit.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "The system was built to forecast wind generation for Xcel's unit-commitment and trading decisions. The cost savings are its intended use.",
  "system": "NCAR wind power forecasting system with the DICast adaptive forecast blending engine",
  "model_version": null,
  "developer": "National Center for Atmospheric Research (Research Applications Laboratory)",
  "deployer": "Xcel Energy",
  "location": "United States",
  "domain": "Energy",
  "segment": "Utility wind generation forecasting",
  "benefit_type": "Improved Task Accuracy",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "organizational",
      "depth": null,
      "reason": "Xcel's US$49 million estimate and the error reduction to 10.8% are reported by the makers and the press without independent measurement."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "Ratepayer savings and coal-unit decommitment are reported as estimates. No audited figure is documented."
    }
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  "ai_role": {
    "status": "unassessed",
    "statement": "The forecasting system's contribution to savings is Xcel's own estimate relayed by NCAR authors and reporters. The AI character of the system rests on UCAR's reference to artificial-intelligence research and DICast's adaptive bias correction, not on an explicit machine-learning description. So both the AI status and the causal share are unverified."
  },
  "evidence": [
    {
      "kind": "news report",
      "citation": "Sue Ellen Haupt and William P. Mahoney, 'Taming Wind Power With Better Forecasts', IEEE Spectrum, 22 October 2015",
      "title": "Taming Wind Power With Better Forecasts",
      "url": "https://spectrum.ieee.org/taming-wind-power-with-better-forecasts",
      "date": "2015-10-22",
      "written_by": "makers",
      "quote": "Xcel estimates that improvements in forecasting have saved customers US $49 million so far."
    },
    {
      "kind": "news report",
      "citation": "Haupt and Mahoney, IEEE Spectrum, 22 October 2015 (on DICast)",
      "title": "Taming Wind Power With Better Forecasts",
      "url": "https://spectrum.ieee.org/taming-wind-power-with-better-forecasts",
      "date": "2015-10-22",
      "written_by": "makers",
      "quote": "it constantly checks its predictions against what actually happened and adjusts its bias corrections and weightings."
    },
    {
      "kind": "news report",
      "citation": "John Fialka and Benjamin Storrow, 'Xcel learns to forecast wind, then shuts coal plants', E&E News, 4 December 2017",
      "title": "Xcel learns to forecast wind, then shuts coal plants",
      "url": "https://www.eenews.net/articles/xcel-learns-to-forecast-wind-then-shuts-coal-plants/",
      "date": "2017-12-04",
      "written_by": "independent",
      "quote": "the forecasts, preparations and timing meant the company could save $10 million in power production costs each year."
    },
    {
      "kind": "press release",
      "citation": "UCAR/NCAR AtmosNews, 'Wind power wisdom', 7 February 2012",
      "title": "Wind power wisdom",
      "url": "https://news.ucar.edu/6460/wind-power-wisdom",
      "date": "2012-02-07",
      "written_by": "makers",
      "quote": "This effort is building on longstanding NCAR research in areas ranging from statistics to artificial intelligence, turbulence theory, and model development."
    }
  ],
  "replication": {
    "produced_by": [
      "Multiple numerical weather models blended with an adaptive statistical layer trained on each wind farm's history",
      "Forecast updates every 15 minutes feeding unit-commitment decisions",
      "A utility with large wind capacity and dispatchable coal and gas units to decommit"
    ],
    "must_hold": [
      "Continuous observed generation data for bias correction",
      "Operators who act on forecast confidence bands",
      "An independent accounting of avoided production costs, absent here"
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "NCAR's DICast cuts Xcel's wind forecast error to 10.8%",
  "metric": "cut mean wind forecast error from about 18% to 10.8% by 2014; Xcel estimated US$49m saved",
  "comparator": {
    "against": "Xcel's wind forecasts in 2008, before NCAR's system",
    "before": "about 18 percent for 18 to 42 hours ahead",
    "after": "10.8 percent",
    "basis": "measured",
    "quote": "In 2008, the year we started working on this problem, the forecast error at a typical wind farm in this region was about 18 percent for 18 to 42 hours ahead, calculated as a percentage of the capacity of each farm. But by 2014, the mean forecast error had fallen to 10.8 percent.",
    "url": "https://spectrum.ieee.org/taming-wind-power-with-better-forecasts"
  },
  "later": {
    "status": "persisted",
    "date": "2017-12-04",
    "note": "By 2017 Xcel was still relying on the NCAR forecasts, which Haupt said saved about $10 million a year in production costs, while it planned coal retirements and more wind.",
    "quote": "According to Haupt, the forecasts, preparations and timing meant the company could save $10 million in power production costs each year.",
    "url": "https://www.eenews.net/articles/xcel-learns-to-forecast-wind-then-shuts-coal-plants/"
  },
  "value": {
    "amount": "US $49 million",
    "what": "saved through forecasting improvements, cumulative to 2015",
    "who": "Xcel Energy customers",
    "estimate": true,
    "quote": "Xcel estimates that improvements in forecasting have saved customers US $49 million so far.",
    "url": "https://spectrum.ieee.org/taming-wind-power-with-better-forecasts"
  },
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
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      "editor": "AIBD"
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      "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
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      "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.",
      "source": "https://spectrum.ieee.org/taming-wind-power-with-better-forecasts; https://www.eenews.net/articles/xcel-learns-to-forecast-wind-then-shuts-coal-plants/; https://spectrum.ieee.org/taming-wind-power-with-better-forecasts",
      "editor": "AIBD"
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      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://ral.ucar.edu/about; https://en.wikipedia.org/wiki/Xcel_Energy",
      "editor": "AIBD"
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          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 29,
      "reviewed": 0,
      "record_mapped": 29,
      "not_assessed": 62,
      "complete": false,
      "by_state": {
        "value_present": 29,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 62,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}