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Efficient Energy Statistics with Structured Statistical Process

Learn the structured statistical process for energy statistics focusing on methodology, metadata use, and quality standards. Understand the chain of actions from input to output in energy data collection and dissemination.

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Efficient Energy Statistics with Structured Statistical Process

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  1. Statistical process as a structured chain of successive actions and intermediate products, supported by the coherent use of metadata  Focused on energy statistics and relation IRES Hans Pouwelse Statistics Netherlands

  2. content • Brief description Business Architecture statistical process • Role of coherent meta data • Focused on Energy Statistics • Relation to IRES

  3. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  4. Conceptual metadataOutput world • Definitions variables and classifications in output terms (‘language’ of users) = definitions rows en colums output tables • Reporting period (year, month…) • Units of measurement • Statistical units (micro level output register)

  5. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  6. Conceptual metadataInput world Questionnaires • Design questionnaires: questions, definitions (in the ‘language’ of respondents) • Reporting period • Observation units (observable units) Registrations (‘administrative data’) • Definitions of variables and type of units

  7. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  8. Process metadata To provide methods and rules for the process to go from stage to stage (from database to database) • Sampling scemes • Methods and rules for editing, validation, imputation, aggregation and disclosure control • Transformation rules to bridge the gap between input concepts and output concepts

  9. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - -

  10. Qualtity metadata • Define quality standards (required output quality) • Rules to measure resulting quality

  11. Focused on Energy Statistics • Energy statistics are normal statistics: logic stages sceme and metadata fully applicable to energy statistics • Some elements specific for energy statistics:

  12. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - - National energy policy International EU (en stat reg) IEA (ESM) UN (IRES)

  13. Conceptual metadataOutput world • Classification Energy products(IRES chapter 3 (SIEC); InterEnerStat, ESM) • Classification Energy Flows, Energy balance(IRES chapter 5 and 8; InterEnerStat, ESM) • Classification economic activity(ISIC, NACE) • Joint Annual Quest, JODI, MOS etc • Units of measurement (Joule, toe, tonnes, kWh etc.) (IRES chapter 4) • Caloric values (IRES chapter 4)

  14. Conceptual metadataInput world Questionnaires • Design energy questionnaires Neth: commodity/energy balance format Registrations (‘administrative data’) • Definitions of variables and type of unitsNeth: client files energy companies (unit= connection adress)

  15. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S 4 2 1 U S E R S 3 5 6 7 8 9 Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a The Statistical Process - - - National energy policy International EU (en stat reg) IEA (ESM) UN (IRES)

  16. Relation to IRES • Attempt to link IRES chapters with a logical place in the business arcitecture sceme:

  17. Meta servers for C o n c e p t u a l M e t a d a t a Input Variables Output Variables The Statistical Process Input-world Output-world D e s i g n S t a g e : m e t a d a t a Micro Level Input-register BaseLine Micro level Output-register MicroBase Macro Level Cube StatBase Macro Level Output Database StatLine R R E E S G P I O S N T D R E A N T T I S O N S U S E R S Publication & Dissemination Aggregation Selection & Data collection Editing & & DisclosureControl & data entry imputation Tabulation Input sphere Throughput sphere Output sphere Implementation stage: data Meta servers for P r o c e s s M e t a d a t a Chapters IRES 3, 4, 5, 8 6b? The Statistical Process 6b? 6a? 6a? - - - 1,2 11 7a 10 9 Metadata Quality 7b?

  18. summary (1) • Statistical process seen as a logical output oriented cycle • Starts with users (identification user needs) • Ends with users (provide desired statistical results) • Structured chain of successive actions • Delimited by intermediate products (logical databases) • Supported by the coherent use of metadata (conceptual, process, quality)

  19. summary (2) • Important to make clear distinction between input world and output world • Explicitly bridge the gap between input concepts and output concepts:-input definitions output definitions-observation units statistical units

  20. summary (3) • Logical model applicable to energy statistics (as being normal statistics) • IRES may be structured according to the lines of the model • Which seems not completely be the case right now! (in particular: distinction input/output world)

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