Measured data analysis: general philosophy

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Measured data analysis: general philosophy

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The objective of this section is to closely compare on-site measured data with simulated values, either in hourly or in daily values. It has a two-sided function:

On the one hand, it has helped us for the validation the software by comparing its results with carefully measured data in 7 installations.

On the other hand, it constitutes a powerful tool for the analysis of the operation of PV systems in use, allowing for the detection and identification of even the smallest misfunctioning.


This involves a much more complex process than the simple system simulation, which includes the following steps:

1.        Importing the measured data:. this is done by a programmable data interpreter, which accepts almost any ASCII file, provided that it holds records of hourly or sub-hourly steps, each one on a single ASCII line. It allows to choose, among the measured variables, those which suit the simulation variables.

2.        Checking the imported data: In order to verify the validity of the imported data file, a number of tables and graphs in hourly, daily or monthly values, may be drawn. Further, some specific graphs often used in PV data analysis (inverter efficiency, input/output diagrams, normalised performances parameters, etc.) are also available, allowing, at this stage, for using PVsyst as a complete tool for the presentation of measured data.

3.        Defining the system parameters: You have to define a project and variant parameters, exactly in the same way as for usual simulation. At this stage you should carefully introduce the real properties of your system.

4.        Comparisons between measured and simulated values: after performing the simulation, you will obtain close comparison distributions for any measured variable. According to the observed discrepancies, you probably will analyse their cause and modify the input parameters accordingly. This offers a powerful way to exactly determine the real system parameters, as well as temporarily misrunnings.

5.        Elimination of break-down events: most of time measured data hold undesired records (break-down of the system or the measurement equipment), easily identifiable on the graphs. These can be selectively eliminated in order to obtain clean statistical indicators - mean bias and standard deviation - corresponding to normal running of the system.