Demand Forecasting and Gas Flow Data NW GRI Transparency Workshop Chris Logue – March 2009

2 Contents  Demand Forecasting  National Grid Approach  Methodology  Accuracy  Market Significance  Real Time Gas Flow Data  National Grid Approach  Market Significance

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4 Weather stations 11 weather stations feed data to the 13 LDZs Scotland Northern North East North West Wales North Wales South West Midlands East Midlands Eastern South West Southern South East London Glasgow Newcastle Leeds Manchester Nottingham Birmingham Cardiff Bristol Benson London Southampton

5 How? - suite of models using different techniques Profile (ARIMA) STF (Complex regression) Neural network ALN (Adaptive logic network) Inday (Simple regression) Bayes (Complex regression) Box 1 (Box Jenkins) Box 2 (Box Jenkins) Sumest (Complex regression) Wintest (Complex regression) D-1 Average weighted according to performance over last 7 days (Combination). Further adjustment made based on recent combination error (CAM) D D D D D

6 Forecasting NTS Direct Connected Loads Input Data  Shippers OPNs/SFNs for NTS direct connected loads, first received at D-1 12:00.  Met Office For D & D-1, forecast temperatures and wind speed For D-2 to D-7, forecast max and min temperatures Forecast is produced for each individual site Models Used  Pass through OPNs where available  Profile model, forecast end of day volume which is then profiled to hourly offtake  regression model, forecast individual hourly offtake  Models are trained every week

7 Demand Forecast Performance NTS Demand Forecast Performance 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% D-7 D-6D-5 D-4 D-3D-2 D-1 13:00hrsD-1 16:00hrs D-1 00:00hrs D 10:00hrsD 13:00hrs D 16:00hrs D 21:00hrs D 00:00hrs

8 Of Use to the Market? Day ahead and within day forecasts have been provided to shippers for more than 10 years D-1 forecast is subject of a performance incentive regime Demand Forecast may  Be used by shippers to supplement / verify their own forecasts  Sometimes be followed by price movements  Indicate requirement for a demand response

9 Conclusions  Demand Forecasting is inherently uncertain due to uncertainties in weather and prices  Forecasting accuracy improves from 7 days ahead to within day as more accurate information becomes available  National Grid demand forecast, although robust at present, faces significant challenges, e.g. increase in wind generation leading to greater uncertainty from CCGT demand

10 Real Time Flow Data National Grid began publishing flow data in 2006 following a lengthy consultation process Strong support from downstream players Reservations from upstream players Flow rate at every major entry point at 2 minute resolution Updated every 12 minutes Available as automated download

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13 Data can also be displayed graphically

14 Of Use to the Market? Gas flow data has; Biggest hit rate of any National Grid web page Been widely reported on and used by the press Become accepted and controversy now largely diminished