Weather Forecasting for Load Forecasts DTN/Meteorlogix.

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Presentation transcript:

Weather Forecasting for Load Forecasts DTN/Meteorlogix

Outline Corporate Overview Forecast Improvement Goals The Meteorlogix Forecast System Statistical Weather Models Manual Input from Meteorologists Multiple data output Benefits Forecast Summary Forecast Preview for the 6-10 Day Period

DTN/Meteorlogix Corporate Overview Commercial weather leader DTN is a leading provider of proprietary business-to-business real-time information services enabling its customers to make “Smarter Decisions” Offices in Omaha, Minneapolis and Boston 110,000 subscribers across Agricultural, Energy and Weather Markets Provide weather information and services to ~700 utilities Stable organization with over 675 employees with strong technology skills and vertical market expertise. ~95 Degreed Meteorologists World’s largest commercial weather service provider, delivering weather information to the Energy industry since 1946 Industry leading technology State of the art weather forecasting system Proprietary load forecast information (Effective Degree Days) Comprehensive GIS weather support

Forecast Improvement Goals: Reduced Mean Absolute Errors

Making Better Forecasts Leverage all available resources Improvements in numerical weather models New technology Experienced forecast staff Combine these into a Forecast System Measure and quantify the results

Making Better Forecasts: Numerical Weather Prediction There are many models No one model is right all of the time Model skill scores continue to improve Forecast system needs to take advantage of these improvements

Making Better Forecasts: The DTN/Meteorlogix Forecast System Automatically create a very good first guess forecast DICast statistical forecasts Incorporate an ensemble of many weather models, and use statistical methods to optimize the forecast Integrating manual input and experience into the forecast Graphical Forecast Editing Focus only where manual input adds value Tools to monitor and manage forecasts Real-time verification and feedback Trend and bias analysis

Statistical Weather Forecasts An ensemble of high- resolution models, MOS, and Dynamic MOS Updated hourly, using current observations Self-learning, error- correction Proven to have lower errors than any individual forecast component

Statistical Forecasts: Better than any single model

Manual Input Graphical editing allows forecasters to see the way they think - spatially Focus on limited areas, and specific times Incorporates terrain and local effects Insures consistency

DTN/Meteorlogix Forecast System Output Forecast values are available anywhere in the CONUS Points Areas Multiple parameters Temperature, dew point, wind, clouds, etc. There are always 15 days of forecast data Hourly Daily

DTN/Meteorlogix Forecast System Benefits Timeliness Forecasts are updated every hour Quality All available forecast data is used in each forecast Makes for the best, most consistent day-in, day-out, forecast Meteorological experience is focused on adding value Extreme events Precipitation and its effects Reliability Forecast products are always current and up to date Scalability Can provide high quality forecasts even without observations

Forecast Summary Weather forecasts are improving The way in which weather forecasts are created is rapidly evolving New forecast processes are making more and different kinds of data available Are there ways to take advantage of new weather data in load forecasts?

High Impact Weather April 17, 2006 All ERCOT Locations AvgHrlyErrMaxErrMinErr Day 1 Average Day 2 Average Day 3 Average Day 4 Average Day 5 Average Day 6 Average Day 7 Average

High Impact Weather April 17, 2006 RptDateRptDayStationAvgHrlyErrActMaxFcstMaxMaxErrActMinFcstMinMinErr KABI KACT KAUS KBRO KCRP KDFW KGLS KIAH KINK KJCT KLFK KLRD KMAF KMWL KSAT KSJT KSPS KTYR KVCT Day 1 Average

High Impact Weather April 17, 2006 RptDateRptDayStationAvgHrlyErrActMaxFcstMaxMaxErrActMinFcstMinMinErr KABI KACT KAUS KBRO KCRP KDFW KGLS KIAH KINK KJCT KLFK KLRD KMAF KMWL KSAT KSJT KSPS KTYR KVCT Day 2 Average

High Impact Weather January 12 th & 13 th, 2007

High Impact Weather Model Data – Jan 13 th, 2007 Model disagreement Jan 12 th – Jan 13 th, 2007 KTYR – Tyler, TX ETA MOS GUIDANCE 1/13/ UTC DT /JAN 13/JAN 14 /JAN 15 /JAN 16 HR N/X TMP DPT CLD OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV WDR WSP GFS MOS GUIDANCE 1/13/ UTC DT /JAN 13/JAN 14 /JAN 15 /JAN 16 HR N/X TMP DPT CLD OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV WDR WSP

High Impact Weather Observed Temperatures

High Impact Weather Results and Conclusions Forecasts did not handle the rapid drop in temperatures well Average hourly error of 5.0 for ERCOT locations Enhanced weather editing mode installed soon Easier editing Quicker results in products Better response to rapid temperature change

High Impact Weather Improvement

DTN/Meteorlogix Contact Information Richard Wilson Director of Energy Services DTN/Meteorlogix Phone: (781) Jeremy Duensing Quality Assurance Manager DTN/Meteorlogix Phone: (952)