Sources: Configure calculated variables

1.      Presentation of calculated variables

 

In the tab "Variable" one can identify easily the variables which one would like to receive in Opisense. The variables (raw variables) that are received via a third system (hardware or other source) and the virtual variables (“calculated” variables) that are calculated via a combination a specific variable of the specific source with other data sources available in Opisense. One can easily distinguish the raw variables from the virtual variables via a symbol indicated in the image here-under.

VariablesCalcul_es.png

E.g. in this case the variable "Phase A comsumption" is a variable indicating a consumption based on the raw variable received: "Phase A Index".

5.png

The interface for configuration is split in two parts:

-       At the left: one can see a configuration tool and select variables that will be combined via the calculation. In this case, one has indicated the sources which will be used to calculate a virtual variable. Apart from variables related to sources that are available in Opisense, one can also define a constant value via the tab Forms.

-       At the right: the code permits to calculate the virtual variable based on the variables that are selected. In this case, the user uses a code in language R.

The code that is used by the client is a standard code in Opisense. This code is a code to caculate the consumpation based on index values.

The engine of the operation is stipulated in the section “Formula”:

6.png

 

We see that the result of the virtual variable will be calculated based on the successive difference of the index values.

The rest of the code is used for:

- Separate the different variables on an time axis (t$t), which will be used for the calculated variable (fonctions GetInputAxis, GetRegularAxis, SetDataOnCommonAxis)

- Filter the result of the calculated variables in order to ignore the points that are exactly the same as those that are already present in Opisense (fonctions FilterNaOutput et FilterDuplicateOutput)

For more detailed explication on the functions and on the working method of the calculated variables, you can follow the link to a Webinar on the calculated variables:
https://www.bigmarker.com/opinum-s-a/Webinar-Variables-calcul-es?bmid=b65631536712

Here-under we provide some important notes in relation with the way to access the input variables:

If ‘x’ is the alias which is given to a input variable via the interface on the left:
- -inputVariables$x$TimeSeries$Dates
permits to access the time axis from the input variable
- -inputVariables$x$TimeSeries$Values
permits to access the values of the input variable
- -inputVariables$x$TimeSeries
permits to access the table with two vectors referred to: at each time stamp, the value

In order to receive more insights on all the functionalities of the interface “Calculated variables”, image that we will calculate the consumption of a value starting from an index value:

 

2. Creation and use of calculated variables:

2.1. Introduction

Imagine that we will calculate a virtual variable for consumption (which we call “Total consumption”) from an index value (wich is called “Active energy” in this example.
In order to start, one will click on "Add a new calculated variable" in the tab Config.

7.png

The following screen asks us to define a new calculated variable:

8.png

2.2. Type of calculation: At this point, only language R is supported. The implementation of other languages is foreseen.

2.2.1. II. Name: Name of the new calculated variable

2.2.2. III. Unit: unit of the values of the calculated variables (when the calculation is performed). One should think about the grandeur of the results of the calculated variables so that the unit chosen is in line with the calculcation.

2.2.3. IV. Granularity: the granularity of the variable indicates the time between each value of the calculated variable (e.g. hourly, daily).

2.2.4. V. Quantity type:
- Instantaneous : characterises an instantaneous variable which has a specific value at a moment in time (typical: stream, frequency, voltage, power, debit ...)
- Integrated: characterises a variable from which the grandeur ensues from an integration (typical: : consommation d'énergie)
- Cumulative : characterises a variable which will only accumulate (typical: a consumption index).
- Minimum : characterises a variables from which the value will be the minimum value in a time interval (typical: minimum stream, minimum frequency, minimum voltage, minimum power, minimum debit, ...)
- Maximum : characterises a variable from which the value will be the maximum value in a time interval (typical : maximum stream, maximum frequency, maximum voltage, maximum power, maximum debit, ... )
- Averaged :characterises a variable from which the value will be the average value in a time interval (typical : average stream, average frequency, average voltage, average power, average debit, ... )

2.2.5. When clicking on “Save”, the interface will switch immediately to the interface for the real configuration of the virtual variable:

2.3. Definition of the variables to include
A first step consists out of the definition to define the variables to include in the calculation.

9.png

2.3.1. Alias: name which will be given to the input variable in the calculation. This alias is used in the syntax: inputVariables$x$TimeSeries.

2.3.2. Site: site to which the input variable is linked

2.3.3. Source: source to which the input variable is linked

2.3.4. Variable : input variable

2.3.5. Granularity: by chosing "Raw", one chooses not to aggregate the values from the input variables. By chosing not for the option “Raw”, the input data will be aggregated based on your choice: minute, hour, day, week, month or year, before sending over the data to the calculation engine. ,

2.3.6. Aggregation: if the granularity is not "Raw", one should choose the method of aggregation: sum, average, maximum, when appearing, variance or standard deviation.

2.3.7. Périod: the period permits to define the quantity of data input which will be send over to the calculation engine, at each new calculation. This period can be defined by using a relative manner (2 days before, the 10 latest measuring points,…) or by using a fixed manner by choosing dates via a calendar.

2.3.8. Unit: the values of the input variable will be converted in the unit chosen, before sending the values to the calculation engine.

2.3.9. As trigger: by activating these fields, one will assure that a new calculation will be performed each time a new value of the chosen input variable enters Opisense. Opinum recommends to always have this option activated for at least one variable, in order to ensure that the calculation engine is activated and the virtual variable will be calculated.

Following the same logic, one can use the forms or define a constant variable as input value. In order to do this, one has to define an alias and will select a from or define a value for the constant.
The two icons permit to delete an input variable or to duplicate the values of the fields in another input variable.

10.png

2.4. Implement the calculation code
The second part consists out of the implementation of the code R which will feed the calculation engine.

The output of the code should have the following form :

list(Timeseries = data.frame(Dates = myDates, Values = myValues))


In this case, we can easily copy the code we used before as we used the same alias for the index variable (which is the only input value in this case), 'vIndex', we can copy the code completely in order to realise the calculation and so to have a value for the consumption.
Please note that the calculation code will send back the values which are not coherent with the units which are chosen for the calculated variable (at step 1 Introduction).

11.png

2.5. Verification tool

12.png

The button "Check" permits to verify the syntax of your code in R.

The button “Visualise” permits to visualise the result via a graph showing the result of your code, based on the calculation.

If you observe a behaviour which is not in line with your expectations, two possible intermediate solution can help to define the problem:

- Once you defined all your input variables, you can click on "Download sample data" in order to download the data set you defined. This data set will be used in the R environment, as for example RStudio. This data set will be send to the calculation engine at each trigger (as defined). The aggregation parameters, the period and the conversion unit are already applicable on this particular data set.

-You can as well make a direct export of you R code by clicking on “Add”. Your code will be directly be exploited in an R environment, like for example RStudio.

These two mechanisms permit to turn your calculation in an R environment with as goal, for example, to use a debug tool.   

3.      Installed packages :

For information, here is the list of the packages installed on the Opisense R Server. Other packages can be added on demand :

RUN R -e "install.packages('Rserve', repos='http://cran.r-project.org')"

RUN R -e "install.packages('ggplot2', repos='http://cran.r-project.org')"

RUN R -e "install.packages('RJSONIO', repos='http://cran.r-project.org')"

RUN R -e "install.packages('rjson', repos='http://cran.r-project.org')"

RUN R -e "install.packages('Rmisc', repos='http://cran.r-project.org')"

RUN R -e "install.packages('signal', repos='http://cran.r-project.org')"

RUN R -e "install.packages('foreach', repos='http://cran.r-project.org')"

RUN R -e "install.packages('doParallel', repos='http://cran.r-project.org')"

 RUN R -e "install.packages('jsonlite', repos='http://cran.r-project.org')"

RUN R -e "install.packages('httr', repos='http://cran.r-project.org')"

RUN R -e "install.packages('chron', repos='http://cran.r-project.org')"

RUN R -e "install.packages('data.table', repos='http://cran.r-project.org')"

RUN R -e "install.packages('quantmod', repos='http://cran.r-project.org')"

RUN R -e "install.packages('oce', repos='http://cran.r-project.org')"

RUN R -e "install.packages('RODBC', repos='http://cran.r-project.org')"

RUN R -e "install.packages('base64enc', repos='http://cran.r-project.org')"

RUN R -e "install.packages('lubridate', repos='http://cran.r-project.org')"

RUN R -e "install.packages('dplyr', repos='http://cran.r-project.org')"

RUN R -e "install.packages('forecast', repos='http://cran.r-project.org')"

 

 

Was this article helpful?
0 out of 0 found this helpful

0 Comments

Please sign in to leave a comment.