DELFOS: GAS DEMAND FORECASTING. Author and speaker: JOSÉ MANUEL GÁLVEZ CAÑAMAQUE

Transcription

1 DELFOS: GAS DEMAND FORECASTING Author and speaker: JOSÉ MANUEL GÁLVEZ CAÑAMAQUE

2 PROJECT : DELFOS DURATION: OCTOBER APRIL 2000 CUSTOMER COMPANY: ENAGAS (GAS NATURAL) OBJECTIVE : GAS DEMAND FORECASTING (DAILY AND HOURLY) STRUCTURE: TOTAL SYSTEM FORECASTING SECTIONS FORECASTING POSITIONS FORECASTING TECHNIQUES: BOX-JENKINS METHODOLOGY PATTERNS RECOGNITION TECHNIQUES

3 grupo apex,, s.a.

4 ! Foundation: 1983, at the Complutense University of Madrid! First: Mathematical and Statistic orientation! Now: Software Engineering! 170 employees (Doctors, Bachelors, Engineers,.) Working with SAS since 1989 SAS Quality Partner since 1997 Some projects in which SAS has been employed: - TELEFÓNICA I+D - UNIÓN FENOSA - BANKINTER - ENAGÁS - ENDESA - IBERIA

5 One of the work fields of Grupo Apex S.A. is mathematical problems modelling, its solution and implantation Other fields where our company works are: Systems development Software engineering and development Consultancy Formation

6 Consultancy In following areas: Logistics: Assignment, Location, Planning... Statistics: Forecasting, Data Mining... Artificial intelligence: Expert Systems Optimitation & Simulation: Strategic Planning, Mathematical Programming...

7 FOUNDATION YEAR: 1972 WORKERS: 930 employees ENTRANCES TOWARDS TRANSPORT INFRASTRUCTURE: Gas pipeline in Calahorra (across the Pyrenees) Magreb-Europa gas pipeline (across Straits of Gibraltar) TRANSPORT NETWORK: High pressure gas pipelines along 5063 Kms POSITIONS: Actually around 200. DISTRIBUTION NETWORK: 2397 Kilometers TRANSPORT INSTALLATIONS ALL OVER PENINSULAR SPAIN

8 TOTAL SYSTEM R B G RED 45 SECTION 1 SECT. 2 SECT. n GAS DEMAND DISTRIBUTION IN PENINSULAR TERRITORY

9 TOTAL SYSTEM MTe

10 PHASES OF SERIES ANALYSIS DATA PREPARATION - QUALITY STUDY. CLEANSING. SERIES STUDY - % INDUSTRIAL DEMAND - % HOME DEMAND - % INTERRUPTION INDUSTRIAL DEMAND DESCRIPTIVE STUDY - TREND - CYCLE - SEASONAL VARIATION - STABILITY 1/2

11 PHASES OF SERIES ANALYSIS MATHEMATICAL MODELLING - INITIAL MODEL DETERMINATION - PARAMETERS ESTIMATION - GOODNESS CONTRAST - INTERVENTION ANALYSIS - TRANSFERENCE FUNCTION MODEL - ORIGINAL MODEL READJUSTMENT FORECASTING STAGE - FORECASTING ERRORS OBTAINING 2/2

12 DATA Possibly one of the hardest parts in the research. Generally speaking, data arrival do not take place with wanted quality. This give rise to a research labour and very detailed job which requires, apart from other aspects, to get qualitative information from the series at issue, continuous comunication with customer in this effect, until the obtaining of data as optimal as possible.

13 Nm 3 STUDY OF DATA QUALITY POSITION = HOUSEHOLD + INDUSTRIAL + POWER-STATION POWER-STATION

14 STUDY OF DATA QUALITY NEGATIVE DEMAND DATA

15 STUDY OF DATA QUALITY Nm 3 ERRORS IN DATA COLLECTION

16 STUDY OF DATA QUALITY Nm

17 Nm 3 DATA CLEANSING DATA FILTERING OF THE YEAR 1998 DEPENDING ON WHAT HAPPENED AT TWO PREVIOUS YEARS IN THE SAME PERIOD

18 TYPE OF DEMAND Whole demand in a series is distributed in different categories: INDUSTRIAL DEMAND CERAMICS, TILES INDUSTRIES, WHICH PRODUCE IN A CONTINUOUS WAY. HOME DEMAND DEMAND WHICH TAKE PLACE IN HOUSES. BASICALLY HEATINGS. INTERRUPTION INDUSTIAL DEMAND INDUSTRIES WHICH ONLY CONSUME GAS DURING CERTAIN TIME PERIODS.

19 INDUSTRIAL DEMAND Nm 3 SERIES CHARACTERISTICS INDUSTRIAL : 100 % HOUSEHOLD : 0 % INTERRUPTION : 0 %

20 INDUSTRIAL DEMAND Nm 3 SERIES CHARACTERISTICS INDUSTRIAL : 11,86 % HOUSEHOLD : 0 % INTERRUPTION : 88,14 %

21 HOME DEMAND MTe SERIES CHARACTERISTICS INDUSTRIAL : 0 % HOUSEHOLD : 100 % INTERRUPTION : 0 %

22 TREND GROWING: Actually, the natural gas market is in continuous expansion in Spain. This fact has got repercussions on growing trend which the most series shows. DECREASING: Actually, there is no series with this characteristic in the natural gas market. NON-EXISTENT TREND: Trend associated with new positions, with scarce history and irrelevant demand in relation to total system.

23 Nm 3 TREND

24 PERIODIC COMPONENTS CYCLE: Component which indicates long term periodicity. Its effect is taken into account by means of a transference function model. SEASONAL VARIATION: Component which indicates short term periodicity. This effect is included in the model applying a stational differentiation to the variable demand.

25 CURVES FITTING TO DEMAND

26

27 Nm 3 WEEKLY SEASONAL VARIATION

28 STABILITY The series with a high percentage of non-interrumption industrial demand are the most regular series. However, when several and mixed external effects take a hand (temperature having an effect on home demand, industries with temporary incorporations, ), finding stability is some more difficult. A new variable could take a hand: psychology and human behaviour.

29 UNSTABLE DEMAND MTe SERIES CHARACT. HOUSEHOLD : 0,54 % INDUSTRIAL : 99,46 % INTERRUPTION: 0 %

30 INTERVENTION ANALYSIS Anomalies showed by daily demand are corrected in the model by means of an intervention analysis. These outliers are caused by: Festivities both on national and regional level. They are treated through pulse variables. Holiday periods, such as Christmas, Easter, August, They are treated by means of step variables.

31 Nm 3 HOLIDAYS FORECAST 1: FORECASTING WITH INTERVENTION MODEL FORECAST 2: FORECASTING WITHOUT AN APPROPIATE INTERVENTION MODEL

32 HOLIDAY PERIODS MTe FORECASTING 1: FORECASTING WITH INTERVENTION MODEL FORECASTING 2: FORECASTING WITHOUT AN APPROPIATE INTERVENTION MODEL

33 TRANSFER FUNCTION MODELS Series with a percentage of home demand above 10% are open to be affected by temperature. This variable has an influence on daily demand in different ways according to the following factors: Season which the day belongs to. Class of the day: workable or holiday. Rise in temperature. Transfer function models take this information and they are the result of diverse intersections between the aforementioned factors.

34 CHARACTERISTICS Series stability CHARACTER 0% 90% 100% INDUSTRIAL 100% 10% 0% HOUSEHOLD Temperature effect Calendar effect Festivities Holiday periods

35 INDUSTRIAL DEMAND SERIES CHARACTERISTICS HOUSEHOLD : 0 % INDUSTRIAL : 100 % INTERRUPTION : 0 %

36 DEMAND INDEPENDENT OF TEMPERATURE

37 HOME DEMAND MTe RELATIONSHIP BETWEEN DEMAND AND TEMPERATURE

38 DEMAND DEPENDENT ON TEMPERATURE

39 HOURLY FORECASTING Starting from the data obtained for daily forecasting, and by means of sophisticated patterns recognition techniques, it is managed to find out hourly patterns for every type of day (workables, Saturdays, Sundays and holidays, ). The said patterns are applied to the corresponding day to obtain the forecast along all and sundry 24 hours.

40 Nm 3 1 st February of st May of HOURS Day s characteristics: Weekday: Monday Type of day: Workable Season: Winter Average temperature: 2.2 ºC Day s characteristics: Weekday: Thursday Type of day: Holiday Season: Spring Average temperature:14.5 ºC

41 SEASON WEEKDAY AUTUMN WINTER MO, FR SA, SU MO., FR SA, SU Data TU,WE,TH TU,WE,TH SPRING SUMMER MO, TU SA, SU MO, FR SA, SU TU,WE,TH TU,WE,TH Temperature Association of forecasts to corresponding group, according to type of day. To apply to forecast the centroid s hourly pattern from the group which this forecast belongs to (percentages) HOURLY FORECASTING Saturday, Winter, ( 10ºC, 15ºC) Friday, Autumn (22ºC, 30ºC) Tuesday, Summer (+35ºC)... Daily forecasting

42 COMPUTER ASPECTS The SAS integration in Delfos has been extremely important, being used different sections from SAS software for the development of every part which composes the project. In the same way, SAS coordination with other software for information processing and data presentation have definitively made possible to constitute the project by means of a easily-run computer application and with very efficient results.

43 REALIZATION OF FORECASTS WITH UNVARIABLE PARAMETERS AF STAT STATISTICAL ANALYSIS EXCEL PLANE FILES EFI SAS DATA SETS ETS GRAPH HOURLY FORECASTING DAILY FORECASTING GRAPHICS BASE HANDLING OF TABLES MACROS

44 COMPUTER STRUCTURE IN DELFOS A SAS PROCEDURE IS EXECUTED FROM VISUAL BASIC, USING AN OLE OBJECT FOR IT. VISUAL BASIC VISUAL BASIC UPDATES AND CREATES ORACLE TABLES. DATA OBTAINING FOR REALIZATION OF FORECASTS, PERFORMANCES OF CONSULTATIONS ON DATA... SAS UPDATES INFORMATION FROM TABLES. AT THE SAME TIME IT OBTAINS DATA FROM ORACLE DATA BASE.

45 José Manuel Gálvez Cañamaque