Statistical analysis: data filtering

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SUMMARY

The discussion focuses on analyzing power usage data from a building over four years, specifically examining the correlation between temperature and electricity consumption. The user aims to filter out this correlation to isolate occupancy effects on usage. Key observations include the identification of an "equilibrium temperature" at approximately 50 degrees Fahrenheit, which serves as a baseline for usage analysis. The user seeks assistance in constructing a line to subtract from the usage data to achieve a clearer profile of occupancy-related energy consumption.

PREREQUISITES
  • Understanding of statistical analysis techniques
  • Familiarity with data visualization tools, such as Excel or similar software
  • Knowledge of correlation analysis and its implications
  • Basic concepts of energy consumption metrics, specifically kWh
NEXT STEPS
  • Research methods for filtering data correlations in time series analysis
  • Learn how to implement linear regression to model energy usage against temperature
  • Explore advanced data visualization techniques to better represent filtered data
  • Investigate the use of statistical software like R or Python for data analysis
USEFUL FOR

Data analysts, energy management professionals, and anyone involved in building performance analysis will benefit from this discussion, particularly those interested in isolating occupancy effects on energy consumption.

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This may be too much work, so I understand if no one tackles the problem...

I'm doing an analysis of the power usage of a building. Sheet 2 of the attached spreadsheet shows typical data (we have 4 years of it). The top graph is the kWh used every 30 minutes of every day in January, 2003. The second graph is the temperature at those times. As you can see, there is a correlation between temperature and usage (the building has electric heat). For example, the light and dark blue diamonds for 1/1 and 1/10 show the highest temperature and lowest usage. What I would like to do is filter-out this correlation. This would leave me with a usage profile that reflects occupancy and pretty much nothing else. All of the lines for kWh would lay right on top of each other most of the time.

Sheet 1 shows some of my efforts to this point. The graph seems to show a corellation between kWh and temp (though, admittedly, not super-strong). Of note, there is something called an "equilibrium temperature." Its basically the temperature at which no heating or air conditioning is required. At this point, the usage will start going up again. So this would serve as a baseline - a minimum usage - for building the corellation. It appears to be at about 50 degrees.

Now, it looks like it should be possible to just construct this line and subtract it from the usage data. But it doesn't appear to be working. Any help would be greatly appreciated.
 

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Hi, russ:
I don't think I can be of any real help here, but when I tried to open the zip-file, I got an "invalid/damaged" message.
It might be something with my computer, but in case it isn't, I thought you should know.
 
Strange - when I try to open it right from the web, it comes up empty, but when I download it, it works. Try downloading it - and thanks for picking up on that.
 

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