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NTS Demand ForecastDSWG - 2 August 2006
Peter Zeng
Network OperationsNational Grid
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Agenda
IntroductionDemand Forecast - OverviewDemand Forecast ModelsForecast PerformanceConclusions
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Introduction
ObjectivesTo fulfil the action DSWG placed on NG to present
“information on forecast development and calculation”
To give DSWG an overview of NG demand forecasting processan appreciation of key information used in the forecasta description of models usedan overview of current demand forecast performance
Reasons for demand forecastingto fulfil UNC obligations including demand attribution processto enable efficient and economic operation of NTS systemto facilitate efficient market operation by providing market participants with the most accurate demand forecast
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NTS Demand Forecast Process
iGMS
Forecaster
Input data Forecasts
Shippers
MIS
CONTROL ROOM
Forecasts Published on Gemini, Information
Exchange, SIS and ANS.
DNCC Met Office
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Forecast of LDZ Demand
It consists of Temperature sensitive loads (NDM) produced by forecasting modelsVLDMC (DM) OPNs provided by shippers
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Weather stations11 weather stations feed data to the 13 LDZs
Scotland
Northern
North EastNorthWest
WalesNorth
WalesSouth
WestMidlands
EastMidlands
Eastern
SouthWest
SouthernSouthEast
London
Glasgow
Newcastle
LeedsManchester
Nottingham
Birmingham
Cardiff
Bristol Benson
London
Southampton
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Which weather factors affect gas demand?
temperaturewind speedrainsnowcloud cover
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Process diagram for producing LDZ Demand forecasts
?PREDICT
?PREDICT
?PREDICT
x 14(13 LDZ + “National”)
GTMS SC2004
MeteredDemand
Weather(Forecast + Actual)
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What is forecast and when?
Within day and day ahead forecasts are run 5 times per day.2 to 7 day ahead ‘Likelihood to Interrupt’ (LTI) run once per day.
ALN and profile models also forecast a volume for each hour
0
5
10
15
20
25
End of day volume, mcm (Area under the curve)
0
5
10
15
20
25
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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
D-1
D-1
D-1
D-1
D-1
D-1
D-1
D-1
Averageweighted according to
performance over last 7 days (Combination). Further
adjustment made based on recent combination error
(CAM)
D
D
D
D
D
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What does a model look like?
PROFILE – WITHIN DAY MODELPROFILE – WITHIN DAY MODELPROFILE model uses the Box Jenkins technique to forecast within day gas demand. There are two different models in the program. Model 1 is usually used for the 10am forecast and model 2 for the rest of the day. However, if the 9am temperature is greater than either the 1pm or 3pm temperature then model 1 is used for the 1pm and 4pm forecasts.Model 1 (at hour k) (used for 10:00 forecast)
∇∇7Dt(h) = w0∇∇7Tt
(3) + w1∇∇7Tt(6) + w2∇∇7Tt
(9) + w3∇∇7Dt(k) + (1-θ1B) (1-θ7B7) at
Model 2 (at hour k) (used for forecasts at other times)∇∇7Dt
(h) = w0∇∇7Tt(h-1) + w1∇∇7Dt
(6) + w2∑1k∇∇7Dt
(j) + (1-θ1B) (1 - θ7B7)at
where Tt(h) is the temperature at hour h on day t,
Dt(h) is the corresponding hourly demand on day t,
at is the error in the forecast demand for hour h on day t,B is the backward shift operator i.e. Byt = yt-1
w0, w1, w2, w3, θ1, θ7 are model parameters..
PROFILE model uses the Box Jenkins technique to forecast within day gas demand. There are two different models in the program. Model 1 is usually used for the 10am forecast and model 2 for the rest of the day. However, if the 9am temperature is greater than either the 1pm or 3pm temperature then model 1 is used for the 1pm and 4pm forecasts.Model 1 (at hour k) (used for 10:00 forecast)
∇∇7Dt(h) = w0∇∇7Tt
(3) + w1∇∇7Tt(6) + w2∇∇7Tt
(9) + w3∇∇7Dt(k) + (1-θ1B) (1-θ7B7) at
Model 2 (at hour k) (used for forecasts at other times)∇∇7Dt
(h) = w0∇∇7Tt(h-1) + w1∇∇7Dt
(6) + w2∑1k∇∇7Dt
(j) + (1-θ1B) (1 - θ7B7)at
where Tt(h) is the temperature at hour h on day t,
Dt(h) is the corresponding hourly demand on day t,
at is the error in the forecast demand for hour h on day t,B is the backward shift operator i.e. Byt = yt-1
w0, w1, w2, w3, θ1, θ7 are model parameters..
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Forecasting NTS Direct Connected Loads
Input DataShippers
OPNs/SFNs for NTS direct connected loads, first received at D-1 17:00.Met Office
For D & D-1, forecast temperatures and wind speedFor D-2 to D-7, forecast max and min temperatures
Forecast is produced for each individual siteModels Used
Pass through OPNs where availableProfile model, forecast end of day volume which is then profiled to hourly offtakeregression model, forecast individual hourly offtakeModels are trained every week
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NTS Demand ForecastingModel types
When For What Horizon How - Model types
Everysite
Every hour
Withinday
End of day regression plus profiling
Every hour Hourly regression (24 models)
Pass through OPNs
Everysite
Every hour
Day ahead
End of day regression plus profiling
Every hour Hourly regression (24 models)
Pass through OPNs
Everysite
Every hour
2-7 day ahead End of day regression plus profiling
Once per day
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NTS Demand forecasting schematic
•Power stations•Industrial sites•Interconnector exports•Storage injection
iGMS
FORECASTER
NTS connected
sites
LDZ off-takes x13
TOTAL NTS Throughput
Shrinkage
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Winter 2005/06 Forecasting Performance
Average Absolute Percentage Forecast Error for Winter 05/06
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
D-7 D-6 D-5 D-4 D-3 D-2 D-1 13:00 D 13:00
Forecasts
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Conclusions
Demand Forecasting is inherently uncertain due to uncertainties in weather and pricesForecasting accuracy improves from 7 days ahead to within day as more accurate information becomes availableNTS demand forecast, although robust at present, faces significant challenges to improve future forecasting accuracy, especially with the introduction of demand forecasting incentive this winter.