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Anakkiin Frequent Visitor
Frequent Visitor

Need help with selecting methods, advanced calculations

Hello,

 

I have these (Azure) tables with these columns:

 

Internal data (measured values from mine sensors in building, new data every 30 seconds):

Temperatures, Humidities, Motions, Lights

 

External data (from web API, outside of the building, new data every 2 minutes):

Temperature, Apparent temperature, Humidity, Cloud cover, Dew point, Ozone, Percipitation intensity / propability / type, UV index, Visibility, Wind speed / gust / bearing.

 

Daily external data (from web API, new data every day):

Sunrise, Sunset, Moon phase, Day length

 

-----------------------------------------------------------------------------------------

 

An I need to find out, calculate and visualize atleast these two things:

 

1. ) Which data affects the internal temperature - not only list, but also dependency strength and order.

 

2. ) Detect when were office window opened - is this even possible? This is more important for my project. For this purposes I did "summary table" which contains this columns:

 

a) Internal, external temperature difference - when this value is decreasing it's more propably that window is opened

b) Internal, external humidity difference - when this value is decreasing it's more propably that window is opened

c) Interal top, bottom temperature difference - when this value is increasing it's more propably that window is opened

 

I tested this assumptions and they worked well. Primary case c) works best. Now I have more than 170000 lines of data from 2019-02-22, but the problem is that I haven't any other information about whether the window was opened or not. If it's not possible to assume that window was 100% opened for my purposes will be enough also when I can calculate some value which will work as probability or something like that (bigger value, bigger chance of opened window), which I will test later, real time... I will open window and look if value is increasing and later, if it's decreasing after close.

 

I am really new to Power BI, data analyze, ... so please I really need you help with anything of this. Also if you can tell me which methods, algorithms, formulas or something like that I must study it will be big help for me. Maybe some marketplace Visuals? Or some scripts?

 

Thank you so much for every reply. If you have any other questions please ask it.

 

Here are my "Summary table"'s data:

Summary table.png

 

I'm very wondering if there is anyone who can help me with this 😞 

3 REPLIES 3
Community Support Team
Community Support Team

Re: Need help with selecting methods, advanced calculations

Hi @Anakkiin ,

Can you please share some sample data with same data structure for test?(you can create a pbix file with a part of records and upload to onedrive or google drive)

How to Get Your Question Answered Quickly

Notice: do mask on sensitive data.

Regards,

Xiaoxin Sheng

Community Support Team _ Xiaoxin Sheng
If this post helps, please consider Accept it as the solution to help the other members find it more quickly
Learning resources: Power BI
Anakkiin Frequent Visitor
Frequent Visitor

Re: Need help with selecting methods, advanced calculations

@v-shex-msft 

 

I forget that, sorry.

 

Here's link for my sample data:

https://stubask-my.sharepoint.com/:u:/g/personal/xvillar_stuba_sk/EeIVds1FaMFHjKY4MkcrejEBN9KsIIyZ9s...

 

Most important table is "Summary table". I hope this will help you help me 🙂

 

Practical problem:

-> Assessing the subjective perception of temperature and other environmental conditions in the workplace

 

And I really don't know where to start or how to start there 😞 

Community Support Team
Community Support Team

Re: Need help with selecting methods, advanced calculations

HI @Anakkiin ,

#1, In fact, I think you can add slicers to choose one date time range who has 'one variable changes obviously and other variables changes smooth' to confirm which variable can effect diff obviously.

#2, You can add a static offset number of temperature/humidity diff, then use this number to compare with temp/humidity diff changes over time. 

For example: current temp/humidity diff and previous row, if it over offset, office window may opened on that datetime.

Regards,

Xiaoxin Sheng

Community Support Team _ Xiaoxin Sheng
If this post helps, please consider Accept it as the solution to help the other members find it more quickly
Learning resources: Power BI

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