datamine analysis tool.xls Tutorial

Transcription

1 INSTITUT WOHNEN UND UMWELT GmbH Annastraße 15 D Darmstadt Germany Fon: (0049) 06151/ Fax: (0049) 06151/ Internet: datamine analysis tool.xls Tutorial Darmstadt, Germany Authors: Tobias Loga, Nikolaus Diefenbach with the support of Contract N : EIE/05/097 Coordinator: Institut Wohnen und Umwelt, Darmstadt / Germany Project duration: Jan Dec 2008 The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Communities. The European Commission is not responsible for any use that may be made of the information contained therein.

2 Content 1 General Remarks Installation Rules for working with the tool General overview: programme features and proceeding Preparation: data selection and import xy-analysis Statistical analysis Examples / exercises Exercise 1: Correlation of calculated heat demand with the calculated delivered energy demands Exercise 2 (based on Exercise 1): Distinction of delivered energy including and not including domestic hot water Exercise 3 (based on Exercise 1 and 2): Determination of the system efficiency Exercise 4 (based on Exercise 1 to 3): Dependence of the delivered energy on the building age Exercise 5 (based on Exercise 1 to 4): Dependence of the expenditure coefficient on the building age...27

3 1 General Remarks This document is a tutorial for the MS Excel software datamine analysis tool.xls which has been developed by IWU in the frame of the Intelligent Energy Europe project DATAMINE. More details about the project you find in the DATAMINE Synthesis Report from December 2006 Concepts for Data Collection and Analysis which can be downloaded from DATAMINE project website The data analysis tool enables an evaluation of databases with the DATAMINE data structure. As soon as the data are collected during the DATAMINE Model Projects the tool will give a general survey of the databases give a survey of the energy performance indicators in a standardised scheme make possible a cross country comparison of energy performance indicators The quantities that can be analysed by the tool will be for example: building envelope: U-values of walls, roofs, floors, windows specific heat transmission losses performance of the systems for heating, DHW, ventilation, cooling, lighting system losses and efficiency factors calculated energy demand per m² of the building and of the systems measured energy consumption per m² CO 2 emissions per m² The functional specifications of the tool are: combination of different databases plausibility checks overall statistics: number of buildings of different categories, building sizes and ages correlation of variables: e.g. energy consumption depending on building size, mean U-value, window size and other parameters, correlation of calculated and measured consumption cross country comparison detailed statistical analyses for building subsets: U-values and system efficiencies as a function of building age and size etc. cross country comparison 3

4 2 Installation Save the zip-file "datamine_analysis_tool.zip" on your computer. Unzip all files and folders to one directory of your hard disk. Check that the sub directory "data" has been created with several test files ("*.db.xls"). Check that the sub directory "LogFiles" has been created with at least one log file ("*.log.xls"). Open the file "datamine analysis tool.xls". When opening a message box "Security Warning" will appear. Please click the button "Enable Macros". Before working with the tool please read the advices in the Worksheet "Info". 3 Rules for working with the tool Editing input cells Please, edit only yellow highlighted cells. Don't change any other cells. Especially rows and columns should not be inserted / deleted! "If you want to copy and paste input data please use the following shortcuts: copy: <Ctrl>-<c> paste: <Ctrl>-<f> = paste special / formulas (don't use standard paste <Strg>-<v>!)" If the copy range comprises several horizontally adjoining cells the selected paste range must have the same width (that means it should comprise the same numbers of horizontally adjoining cells, not only one cell). Never cut and paste input cells! Never use <ctrl-x>! You can delete the data in a range of input cells by selecting the whole range and then clicking the button "Clear selected input data". The selected range may comprise also headlines or other cells - only the input cells (yellow) are affected by this clearing. (This feature does not work when you work with splitted panes and the button is in a different pane.) The content of single input cells or ranges that comprise nothing but input cells may also deleted by pressing <del> as usual. Further options If you want to perform different analysis based on different data fields you can make a copy of this analysis workbook. The name of the analysis workbook is not fixed, you may change it as you want. By clicking the button "Clear all input data" the Sheet "Analysis" will be prepared for a new analysis. By clicking the button "Log..." you can save the definitions and results of the analysis in a log file. The path and file name is defined in the sheet "Settings". Only for MS Excel 2003 and higher versions the respective charts can be copied. If you work with other Excel releases you 4

5 will have to copy the diagram and paste it as "pciture (enhanced metafile)" in a Word document. You may later copy the logged definitions back into the input fields of the sheet "Analysis" (please use paste special / formulas <Ctrl>-<f>, see above) 4 General overview: programme features and proceeding 4.1 Preparation: data selection and import Databases to be analysed First you have to select the databases to be analysed (fig. 1). All databases should be formatted according to the DATAMINE database standard (see extract in fig. 2). You may use the file "test database - country A.xls" as a template. The databases have to be located in a specified sub directory. The name of the sub directory is predefined ( data ) but can be changed if necessary. The database names can be selected by a dropdown list. If another database is added to the sub directory the dropdown list has to be updated by the respective button. In principle an analysis of a database with different data field names or additional data fields is possible. You find more information in the sheet Info of the tool. fig. 1: Selection of data bases to be analysed 5

6 fig. 2: Examplary extract from a DATAMINE database (workbook test database - country A.db.xls ) ID_dataset H_transmission H_bridges A_wall U_wall ecarrier_1_type ecarrier_1_use ecarrier_1_m ecarrier_1_c de-pk03-1 de-pk03-2 de-pk03-3 de-pk03-4 de-pk03-5 de-pk03-6 de-pk03-7 de-pk03-8 de-pk03-9 de-pk03-10 de-pk03-11 de-pk03-12 de-pk03-13 de-pk , ,9 1, , ,6 1, , ,4 2,11 312, ,55 1,8 22, ,2 1,03 382, ,068 1, , ,8 1,73 617, ,49 1,8 588, ,61 1,55 636, ,3 1,52 760, ,428 1,52 349, ,74 0,58 306, ,9 1,03 791, ,4 1,25 gas ,57862 gas ,89828 gas ,0849 gas ,54781 gas gas ,45926 gas ,1421 gas ,67595 gas ,22221 gas , ,05268 gas ,6202 gas ,7317 gas ,88627 gas , Datafields to be analysed The next step is the selection of the data fields that will be analysed: "Selection table": in case of the DATAMINE format always "datamine" "Selection variables": selection of the data field names to be analysed "Limits plausibility check": selection of the lower and upper limit of the plausibility check. "Predefined evaluation ranges": If evaluation ranges for the statistical analysis (see below) already exist for a variable they are shown here. Changes and adding more ranges is possible in the sheet "Settings". fig. 3: Selection of variables to be analysed 6

7 Composed variables In this area additional variables can be defined which are composed of variables from the database (fig. 4). The formulas can be edited in the sheet "Settings". Please use the Microsoft Excel syntax (fig. 5). fig. 4: Selection of composed variables fig. 5: Definition of composed variables (sheet seetings ) Data import By clicking the button "Load / refresh selected data" the selected data from the selected databases will be loaded into the analysis tool. You find the imported data in the sheets "Database 1", "Database2",... As a last data field the variable "check plausibility" will be added, which will have the value =1 when the check result is positive and =0 when it is negative. At the end the date of the data import will always be added. Due to the plausibility check for each value the import of data will take some time. The time depends on the number of data fields and data sets as well as on your computer resources. In order to accelerate this procedure it is recommended to close other Excel applications before starting the data import. 7

8 4.2 xy-analysis By the xy-analysis the dependence of two variables can be determined. The required definitions and are shown in fig. 6 Overall filters These filters are defined for the whole xy-analysis. The following filters may be selected: "Database": filtering of single databases (without selection all imported databases will be analysed) "Parameter": filtering of variables. If you want to apply the plausibility check (see above), you must set: check_plausibility = 1. Data to be analysed There are 4 analysis ranges with 1 x-variables and 1 to 5 y-variables. For each xy pair a supplemental database filter may be applied. Without input all imported databases will be evaluated. fig. 6: Required definitions for the xy-analysis 8

9 Performing the xy-analysis By clicking the button "Perform xy-analysis" the analysis will be performed with the selected boundary conditions. As an output you find all xy value pairs in the sheet "xy1 Diagram Values" which makes them available for the respective charts. Results In the area Results (fig. 6) you find the output for a linear trend. Titles diagram You can choose which xy pairs you will show in the charts (checkboxes in fig. 6). Furthermore you can put in the diagram titles (e.g. in your language). fig. 7: Example for an xy-analysis 9

10 4.3 Statistical analysis In fig. 8 the definitions are shown that you need in order to perform a statistical analysis. fig. 8: Statistical analysis 10

11 Parameters First define the variable which you want to analyse (fig. 9). Then you select up to 4 statistical functions that are to be applied to this variable (the list of functions is shown in fig. 10). If you need less you may hide the respective result rows by deselecting the checkbox "Show". The font size, type and colour as well as the number format of the results can be defined to make the result matrix easier to read. fig. 9 Selection of statistical functions / specification of the output matrix number format (sheet Analysis ) fig. 10: Definitions of relation symbols and statistical functions (sheet Settings ) Overall filters The overall filters are defined for the whole xy-analysis (fig. 8). The following filters may be selected: "Database": filtering of single databases (without selection all imported databases will be analysed) "Parameter": filtering of variables. If you want to apply the plausibility check (see above), you must set: check_plausibility = 1. 11

12 fig. 11: Definition of evaluation ranges (sheet seetings ) Performing the statistical analysis / results You may analyse the dependence of the selected variable from 1 or 2 other variables (e.g. for frequency distributions). For this you have to select one or two variables in the upper left corner of the result matrix per dropdown list. These fields may also be empty if you want to make a simple analysis (for example only one medium value). Please, take care that you have actually loaded the data you want to analyse (see area above: "Datafields to be analysed"). In the sheet "Settings" you may change the evaluation ranges of the parameters. You may also add further parameters and their evaluation ranges. By clicking the button "Perform Statistical Analysis" the analysis will be performed with the selected boundary conditions. The output of the analysis appears in the range "Results" which is described above. 12

13 Statistical analysis charts The output of the result matrix is also displayed in the charts 1 to 4. There are several input fields for the titles of the diagrams. fig. 12: Example for a statistical analysis 13

14 5 Examples / exercises The following examples will demonstrate the application of the analysis tool. They are based on the example databases provided with the tool. For an easier access to the using of the tool you may reproduce the described proceeding step by step. 5.1 Exercise 1: Correlation of calculated heat demand with the calculated delivered energy demands 1 Save a new copy of the analysis tool Copy the file datamine analysis tool.xls to the same folder and call it datamine analysis - example 1.xls. 2 Clear former input data Press the following button in order to clear all input data from the previous analysis: 3 Select the example database A 14

15 4 Select the datafields to be analysed and define the limits of the plausibility check 5 Select the composed variables and the limits of the plausibility check 6 Load the above selected data Click this button: 7 Check the loaded data Select the sheet database 1 and check if the data have been correctly transferred and calculated. 15

16 8 Select the data fields for the xy analysis 9 Perform the xy analysis Click this button: 10 Check the numerical results of the analysis and the chart 16

17 5.2 Exercise 2 (based on Exercise 1): Distinction of delivered energy including and not including domestic hot water 11 Define two composed variables for the calculated delivered energy demand per m² Select the sheet Settings and define the composed variables (red framed) by typing the formulas as described: The syntax of the functions is referring to the MS Excel functions. You have to use the English syntax even if you use Excel versions in other languages. In this example the Excel-function mid is used which determines the first two characters of the data field ecarrier_1_use (in our example = space heating and DHW / = only space heating). 12 Select the two new composed variables Go back to the sheet Analysis, select the two newly defined composed variables and define the respective plausibility limits 17

18 13 Load the above selected data Click this button: 14 Check the loaded data of the additional variables Select the sheet database 1 and check if the data have been correctly transferred and calculated. 15 Select the new data fields for the xy analysis 16 Perform the xy analysis Click this button: 18

19 17 Check the numerical results of the analysis and the chart The delivered energy demand for heating and hot water is about 20 kwh/(m²a) higher than the delivered energy for heating only. 19

20 5.3 Exercise 3 (based on Exercise 1 and 2): Determination of the system efficiency 18 Define two composed variables for the expenditure coefficients Select the sheet settings and define the delivered energy expenditure coefficients for the heating system H and for the combined heating and hot water system HW (red framed) by typing the formulas as described: 19 Select the newly defined composed variables and define the respective plausibility limits 20

21 20 Load / refresh the above selected data Click this button: 21 Check the loaded data of the additional variables Select the sheet database 1 and check if the data have been correctly transferred and calculated. 22 Select the new data fields for the xy analysis 21

22 23 Perform the xy analysis Click this button: 24 Check the numerical results of the analysis and the chart 22

23 The charts show how the system expenditure coefficient depends on the heat demand and of the building size. 23

24 5.4 Exercise 4 (based on Exercise 1 to 3): Dependence of the delivered energy on the building age 25 Additionally select the composed variable year_building 26 Load / refresh the above selected data Click this button: 27 Check the loaded data of the additional variables Select the sheet database 1 and check if the data have been correctly transferred and calculated. 24

25 28 Select the variable to be analysed and the statistics 29 Select the predefined evaluation ranges 30 Perform the statistical analysis click the button: 25

26 31 Check the result matrix The calculated delivered energy per m² depends on the building age and on the building size 26

27 5.5 Exercise 5 (based on Exercise 1 to 4): Dependence of the expenditure coefficient on the building age 32 Change the variable to be analysed 33 Perform the statistical analysis click the button: 34 Check the result matrix The expenditure coefficient only slightly depends on the building age and on the building size. 27