Hello,
I'm about to start a new industrial project in which we want to estimate (interpolate) the INDOOR temperature at unknown 3D positions in possibly complex 3D room geometry based on sparse temperature measurements. For example, see the attached simplified 2D image but remember that my real problem is in 3D (sensors are at known fixed (x,y,z) positions and temperature need to be estimated at arbitrary (x,y,z) position).
I found lots of papers related to kriging in the field of geophysics but these methods are quite complex and do not usually support geometric constraints like indoor room geometry.
I know FEM might be related to my problem but I'm not really interested in complex heat transfer modeling (diffusion, convection, etc.), just simpler instantaneous estimation which I could compare to experimental measurements.
Some kind of kNN with inverse distance weighting might be sufficient and support geometric constraints easily enough?
My goal is to find a good enough method, to be validated experimentally, not necessarily the best and most precise method.
Here is the kind of information I'm looking for:
- Some review or STAR article about indoor temperature modeling/measurement.
- In increasing order of "complexity" and realism, what interpolation methods would you use?
- Known software, library/toolkit that implement such methods.
Regards,
Bruno
Surface pattern variances, shape and type have an effect on temperature level differences.
Hello Adel I . F Mohammad ,
Your comment is just a list of generalities sadly not very helpful... Would you have anything more specific regarding what I was looking for?
Here is the kind of information I'm looking for:
- Some review or STAR article about indoor temperature modeling/measurement.
- In increasing order of "complexity" and realism, what interpolation methods would you use?
- Known software, library/toolkit that implement such methods.
Hi..I am very sorry dear, I do not have this kind of data. Thank you.
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