weighted calculated of three measures from sensor data

[arifrahman]

I have three values (Temperature , Humidity and C02) which i would like to sum up in order to provide an air quality score value

the values of temperature are 

cold = less than 21degrees, normal = 21-24, hot = more than 24

Humidity 

Dry = <30% , Good = 30-60%, Humid = >61%

C02

Optimal = <800ppm, Intermediate = 800-1200ppm, Bad = >1200ppm

I would like to create air quality score as a % out of 100, with each metric type providing an equal and fair share to the overall air quality (so in essence 1/3 or 0.333 each) whilst also factorying in the unique rules of how hot or dry or optimal a unit of measurement is respectively according to the above. 

the data i have is sensor data - you can assume the columns are:

Data Type -  i.e temperature, humidity, C02
Measurement - i.e 22(degrees), 77(%), 800(ppm)

Timestamp- i,e 17/07/2019 11.45 (repeated for each measurement that comes in

hope you can help!

[Anonymous]

---

@arifrahman wrote:

I would like to create air quality score as a % out of 100, with each metric type providing an equal and fair share to the overall air quality (so in essence 1/3 or 0.333 each) whilst also factorying in the unique rules of how hot or dry or optimal a unit of measurement is respectively according to the above. 

Mate, are you asking us to create a metric for you? If you do, then... good luck 🙂 We're not here to create business rules but IMPLEMENT THEM IN DAX. So, in essence, we're here to create algorithms for well-defined business problems. Your problem is not well-defined.

Best

Darek

[arifrahman]

i think what i do mean is how do i implement this in DAX. apologies for the poor explanation.

a DAX Formula to calculate on a single column (the sensor measurement) based on the data type column(the sensor metric i.e temp/humidity) , which would then be tranformed into a % 

any help on how this would this be written in DAX would be greatly appreciated.  this is essentially a metric im trying to extrapolate from my data but struggling to formulate it.

if im not phrasing this correctly i do apologise.

cheers!

@Anonymous