Terms of Reference 1' Focus on illustrating how statistical methods are used to solve business probl - PowerPoint PPT Presentation

1 / 47
About This Presentation
Title:

Terms of Reference 1' Focus on illustrating how statistical methods are used to solve business probl

Description:

weighted according to performance over last 7 days (Combination) ... PROFILE model uses the Box Jenkins technique to forecast within day gas demand. ... – PowerPoint PPT presentation

Number of Views:41
Avg rating:3.0/5.0
Slides: 48
Provided by: Shep165
Category:

less

Transcript and Presenter's Notes

Title: Terms of Reference 1' Focus on illustrating how statistical methods are used to solve business probl


1
Terms of Reference 1. Focus on illustrating
how statistical methods are used to solve
business problems and how statisticians
interact with colleagues and clients to achieve
this. 2. Descriptions of past and on-going case
studies3. Short introductions to their
organisations and to the diverse roles of the
organisations statisticians,
Historic Basics
Models
WEATHER from Met Office (Actual and forecast)
2
Reading UniversityRSS 15th June 2005
  • Shanti Majithia
  • Forecasting Development Manager
  • Wokingham, Berks

UK Transmission
3
Agenda
  • My Background
  • Company Background
  • Application of Statistical techniques within the
    Company
  • University and Project work
  • Conclusion

4
My Background
  • Further education in London Maths Stats and
    Computing
  • Market Electricity Load research, Manpower
    planning
  • Operational Forecasting (Short Time Scale)
  • Liaison with students and Uni. to assist in data
    and direction
  • Presentations Research paper and Forecasting
    conferences
  • Wind Energy, Climate Change, Heating and Cooling
    Load ( Air Con)
  • Risk management
  • Short term Gas Demand and Supply Forecasting
  • Translating data, analysis and information into
    decision making tools

5
National Grid Transco - principal activities in
regulated electricity and gas industries
UK EW transmission GB Gas Transportation LNG Gri
dCom
USA NEESCom
Zambia 38.6 CEC (Copperbelt transmission)
Australia Basslink (Interconnector to Tasmania)
Argentina 27.6 Transener
6
National Grid - UK Electricity
Over 13,000 circuit km of 400
275kv transmission lines and cables
Over 21,000 Transmission Towers
300 substations
Fibre optics
7
Electricity
  • Balance generation and demand efficiently
  • Ensure quality and security
  • Non stop process

Keeping the lights on
8
Electricity Transmission Elements
Power Station
Generator
23kV
400kV
Transformer
Transformer
132kV
Medium
Large Factories,
Factories,

Heavy Industry
Light Industry
To Small Factories, Farms, Residential Areas
and Schools
33 kV
11 kV
240 V
96/29355 ISSUE A SH. 1 OF 1 30-04-99
9
The UK Gas Industry Model
Competitive
Monopoly
Gas supply
Independent transmission
  • Producers
  • DFOS
  • Storage Operators
  • Shippers
  • Traders

TRANSCO
40 of Distribution TRANSCO IPGTS
Suppliers
Energy Companies
Regulated Systems
10
Gas National Transmission System (NTS)
  • 6,600km 450-1220mm diameter pipeline
  • High strength steel X65-X80
  • Operating pressure design70-94bar
  • 7 Transco terminals
  • 24 compressor stations
  • 400 above ground installations (AGI)
  • Key Stats
  • Max demand 02/03 205 GW
  • Peak Demand (1/20) 240 GW
  • Energy Supplied 1150 TWh/yr

11
Gas From Beach To Meter
12
Real Time System Operation in Gas and
Electricity..
  • Balance supply and demand efficiently
  • Facilitate the market
  • Ensure quality and security
  • Maximise system capacity
  • Non stop processes
  • BUT
  • Gas can be stored gt daily balancing
  • Electricity cant gt real-time balancing

13
Application of Statistical Techniques within NGT
  • Data collection - live metering, market
    intelligence and field measurement
  • Data mining e.g. Kohonen SOMs, Genetic
    Algorithms.
  • Forecasting Methods
  • Regression, Box-Jenkins, Bayesian, Neural
    Network (MLP ALN), Curve fitting and
    Holts-Winters, Arch and Garch
  • Probability and Risk Management
  • Liaison to keep abreast of modern methods e.g.
    Statistical methods
  • Management Information System

14
Area of Application of Statistical Techniques
  • Forecasting Energy Demand
  • Trading advice
  • Minimising of volatility
  • Management of probability and risk
  • Calculating and calibrating climate sensitivity
  • Health of the assets in terms of the return
    period
  • Simple use of statistical methods in plant
    reliability
  • Responses on the efficiency of the equipment

15
Electricity Forecasting Techniques
  • Multiple linear regression
  • Last 3 years of historic data
  • Summer (BST) and winter (GMT)
  • Weekdays / Sat / Sun
  • Special days excluded
  • Conventional and Trend models
  • 120 models per annum
  • Interpolation between cardinal points for half
    hourly resolution

16
Forecasting Tools
  • Oracle database
  • Weather and demand feeds
  • StatGraphics
  • EViews
  • SAS
  • PREDICT Forecaster
  • Clementine
  • NN and ALN
  • Genetic Algorithm Library (MIT)

17
The Forecasting Process
Weather Input
Mathematical StatisticalModels
The Forecast
Historical Demand Input
18
Demand - Influences
  • Seasons/ Weather
  • Exceptional events
  • TV

19
(No Transcript)
20
The Effect of Temperature on Demand
7000
6000
5000
4000
COLD High Demand
Demand Effect (MW)
3000
HOT High Demand
2000
Comfortable
1000
0
0
2
3
4
6
9
1
5
7
8
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Temperature
Degrees Centigrade
21
The Effect of Illumination on Demand
4500
DULL High Demand
4000
3500
3000
Demand Effect (MW)
2500
2000
1500
1000
BRIGHT Low Demand
500
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
125
130
135
140
145
150
155
160
Logarithmic Function of Illumination
22
Four Weather Variables
  • Average Temperature TO average of 4 spot
    hourly temperatures up to current hour
  • Effective Temperature TE TO lagged to 50 with
    TE from 24 hours previous
  • Cooling Power of the Wind CP empirical
    combination of temperature and wind speed
  • Effective Illumination of the Sky EI
    (EIMI-ID), where ID is a function of visibility,
    numbers and types of cloud layers and amounts of
    precipitation and MI is maximum illumination. In
    the logarithmic domain.

23
Winter Week Day Peak Demand ModellingMultiple
Regression Model Of Demand
An econometric regression model of the weekday
darkness peak is determined on the four previous
winters demand weather data
Weekday Darkness Peak Demand
Mean Darkness Peak Demand
Weather Dependant Demand
Day of Week
Seasonal trends
error terms
The days affected by Christmas New Year
holidays are excluded from the sample
24
Weather Dependant Demand
? ?TEt?2TEt2?EIt?CPt
Weather Dependant Demand function
25
(No Transcript)
26
Example of a Weather forecast data
27
Gas Forecasting - suite of models using
different techniques
28
What Does a Gas Model Look Like?
29
NTS Supply Forecasting Model types
30
What is a Holts-Winters Model?
31
Understanding Data Questions
  • What to look for in the data before preparing
    forecasts
  • How to treat data when problems are recognised
  • How to prepare forecasts using different models
    and techniques
  • When each forecasting model is appropriate
  • How to use forecasts effectively after they are
    prepared

32
Key Questions
  • Why is a forecast needed?
  • Who will use the forecast, and what are their
    specific requirements?
  • What level of detail or aggregation is required
    and what is the proper time horizon?
  • How accurate can we expect the forecast to be?
  • Will the forecast be made in time to help
    decision making process?
  • Does the forecaster clearly understand how the
    forecast will be used in the organisation?

33
Projects and Case Studies
The seasonal forecast of electricity demand
a simple Bayesian model with climatological weathe
r generator Sergio Pezzulli, Patrizio Frederic,
Shanti Majithia,
34
Data mining --- Clustering of Electricity Profiles
Coloured areas are clusters, each with a
distinctive daily demand profile. Red text is
their interpretation.
35
Clustering of Gas Profiles
Kohonen Network (SOM) Analysis
Jan Dec
Jan Feb Mar Nov
Apr May Oct
June July Aug Sept
Yellow-ish areas indicate similar profiles,
Red-ish areas indicate more varying profiles.
36
North Thames LDZ, Early Jan 2003
37
New up coming Challenges Windpower
  • Variable
  • Uncertain
  • Uncertain uncertainty
  • Danger possibility of sudden loss
  • Weather differences can be at finer geographic
    resolution

38
Volatility and UncertaintyHow best to model?
Ensemble forecasts?How to make operational
decisions?
39
Site Clustering
  • Site clustering can be used to produce a more
    accurate national prediction by taking local
    conditions into account
  • The main way of achieving this is to have a
    reference farm which is representative of the
    cluster
  • It is possible to then use cluster predictions as
    inputs to a national model or simply upscaled
  • One further thought is to forecast both a
    reference farm and a cluster separately and use
    them to create a more stable regional prediction

40
Daily Load Forecasting using ARIMA-GARCH and
Extreme Value TheoryUniversity of
Loughborough EPSRC Project
41
Application
  • Climate Change Impacts on Electricity demand can
    be categorised into a long term (monthly) and
    short term (daily and hourly) load forecast.
  • Long term load forecast using the multiple
    regression approach completed. The results are
    satisfactory. 80 years projection requires the
    UKCIP scenario and BESEECH data (population, GDP,
    consumer spending).
  • Short term load forecast using Box Jenkins and
    Extreme Value Theory is also completed. Waiting
    for hourly climate data from BADC and CRU before
    we can extend our daily/hourly projections to
    2080s.

42
ARIMA (p, d, q) Model
  • The AutoRegressive Integrated Moving Average
    (ARIMA) model is a broadening of the class of
    ARMA models to include differencing.
  • Reason daily and hourly pattern are volatile and
    shows a strong seasonal pattern. p no. of
    autoregressive terms, d the number of
    non-seasonal differences and q no of lagged
    forecast errors in the prediction equation.

ARIMA(1,1,1) is used
43
Probability Distributions
  • nt is a standardized, independence, identically
    distributed (iid) random draw from some
    probability distributions.
  • 3 distributions are used for this purpose-
  • a) Normal
  • b) Student-t
  • c) Extreme Value Distribution
  • For quantiles gt 0.95, extreme value distribution
    is used.

44
Example of Scenario Forecasting (with max and
min scenarios)
45
Combination of Distribution-- ExampleLink
between Annual Peak and Weekly Peak
Winter ACS Median
12 Area cut off Weekly Peak Distribution
The density traces shows how the median of the
simulated winter peak distribution cuts off an
area of about 12 on the corresponding
distribution of simulated weekly peak demands.
46
Probability Distribution
47
Conclusions
  • Various statistical applications demonstrated
  • Wide variety of Statistical method used in data
    rich Energy business
  • Opportunity for Statistician/Business Analysis
Write a Comment
User Comments (0)
About PowerShow.com