provide information Data sources and data collection – a first draft Wolfgang Bittermann Directorate Spatial Statistics Helsinki 24 October 2012

2 | 24 October 2012 The content 3 Subchapters 1. Types of data sources 2. Data collection 3. Country practices Open questions 1. Extension: nr of pages, font type etc. 2. Level of detail 3. Links to other manuals e.g. Energy efficiency manual (IEA), MESH (EUROSTAT) vs independent publication

3 | 24 October 2012 Types of data sources - Overview 1.Administrative data sources 2.New Surveys 3.Additional questions to already existing surveys 4.Metering  Smart meters and smart grids  In situ measurements 5.Models 6.(Integrated Approach?)

4 | 24 October 2012 Types of data sources – Administrative data 1.Registers for public administration 1. Population register 2. Housing register 3. Building register 4. Car register 2.Private registers 1. Sales registers 2. Customers registers 3.Tax information 4.Customs data

5 | 24 October 2012 Types of data sources - New Surveys i.Legal base a.obligatory b.voluntary ii.Type a.Census b.Sample survey iii.Respondents a.Suppliers b.Consumers iv.Format a.Paper b.Web based (electronic questionnaire) c.Interview o Personal interview » Computer assisted (CAPI) » Non computer assisted o Telephone interview » Computer assisted (CATI) » Non computer assisted

6 | 24 October 2012 Types of data sources – General structure 2.New Surveys Advantages Disadvantages I. Legal base a.Obligatory Advantages Disadvantages Text Summery and conclusions b.Voluntary II. Type

7 | 24 October 2012 Types of data sources – Example Consumer Surveys (households)  Advantages  Comprehensive information on all fuels used in private households  Best achievable data quality if they are well prepared and combined with a comprehensive data validation process  Can be used directly and as input for model calculations  Disadvantages  Resource intensive  Expensive  Time consuming  High respondent burden

8 | 24 October 2012 Types of data sources – Example Consumer Surveys Conclusions and summery  The 4 main elements to achieve good results are a careful preparation, a simple questionnaire, well trained interviewers and comprehensive data validation.  Grossing up procedures can be improved by using supplier information, e.g. gas meters attributed to households.  Model based extrapolation e.g. heating degree days help decrease survey frequency.

9 | 24 October 2012 Data collection General aspects  Preparation/Questionnaire o Complexity o Length/Completeness o Structure o Necessary explanations o Announcement  Data validation/imputation/grossing up o Comprehensiveness o Documentation o Use of additional information

10 | 24 October 2012 Data collection New surveys/questionnaires  User and expert implementation into questionnaire development  Taking into account respondents knowledge  Test survey  Taking into account resource availability (budget, staff) and data quality aspects (by choosing type and sample size)

11 | 24 October 2012 Data collections Metering  When metering  What metering o Electricity o Natural gas o District heating 4. Modeling  When modeling  What modeling  Necessary preconditions

12 | 24 October 2012 Country practices Harmonised format? How many examples? Results and metadata included?  Directly implemented  As annex  As link What level of detail – e.g. different levels already available for Austria (Metadata reports for UNSD, EUROSTAT, IEA, national) Shall quality aspects be included?  Directly implemented  As annex  As link

13 | 24 October 2012 IEA-Examples

14 | 8 March 2012 Please address queries to: Wolfgang Bittermann Contact information: Guglgasse 13, 1110 Vienna phone: +43 (1) fax: +43 (1) Thank you for your attention