This document discusses obtaining and analyzing utility energy data from various sources to track energy usage and costs. It outlines a three step process: 1) Obtain raw utility data from sources like utilities and gas companies, 2) Prepare the data by sorting and normalizing it, and 3) Extract meaning from the data by manipulating it in Excel or importing it into software to compare usage over time, between facilities, and to historical averages to identify equipment issues or energy reduction opportunities. The end goal is to have accessible and meaningful reports on facility, meter, and municipal energy usage and costs.

1. Energy Tracking
in Alberta
Obtaining & Using Your Data
Presented by Eric Hennessey
eric@arcticfoxenergy.com

2. Why track energy?
• To know energy use & cost breakdown of
every facility
• To catch costly equipment malfunctions early
• To easily report usage, cost, and emissions

3. Giving Data
Meaning

4. • Working with BC cities since 2008
• Recently setup the Town of Valleyview
– Obtained historical data 2004 to present
• AMSC
• TransAlta
• Local Gas Utility
• Determined best way to obtain & update data
• Extract meaning from the data
Background

5. Step 1: Obtain raw utility data

6. DCM

7. DCM

8. DCM

9. DIM

10. Delivery Cost

11. Energy Cost

12. Demand

13. Demand

14. Demand

15. PF

16. Raw Data
6 files in this example

17. Step 2: Prepare data files

18. DCM
• Sort by Trans Date
• Keep all rows from last update
• Key column: “Usage (kWh)”

19. DIM
• Sort by Date
• Keep all rows from last update
• Key column: “kWh”
• Be aware: hourly data, not daily data

20. Delivery Cost
• Sort by Invoice Date
• Keep all rows from last update

21. Energy Cost
• Sort by Invoice Date
• Keep all rows from last update
• Ignore Consumption

22. Load
• Sort by To Date
• Keep all rows from last update
• Key column: “Metered Demand (kW)”

23. PF
• Sort by To Date
• Keep all rows from last update
• Key column: “Metered Power Factor”

24. Excel
**Month Normalization**
• Raw utility data is organized by READ DATE
• Meters are read sporadically throughout the year
• Usage and cost data is for reading period
(ex: September 11 - October 15)
• Need usage PER MONTH for meaningful tracking

25. Excel
**Month Normalization**
• Example:
– 60 GJ from August 20th to September 10th
– 80 GJ from September 11th to October 15th
– Therefore usage in September:
60 GJ * (10 days / 22 days) +
80 GJ * (20 days / 35 days) = 73 GJ
September
Period 1
Period 2

26. Step 3:
Extract meaning from data

27. 1. Manipulate with Excel, or
2. Import into self-managed software, or
3. Work with an energy tracking and data
visualization consultant
3 main options:

28. • Compare between facilities
– Identify energy reduction candidates
• Compare to history
– Catch spikes in usage for investigation
(equipment malfunctions can cost $$,$$$.$$)
• Factor in climate
• Have facility/meter cost & usage at fingertips
• Have municipality cost & usage at fingertips
Desired
Outputs

29. Example
Result

30. Alerts &
Summaries

31. Facility
Comparison

32. Historical
Comparisons
3y Min
3y Max
3y Average
Usage

33. Historical
Comparisons
Why?

34. Summaries

35. Thank you!
Questions?
Eric Hennessey
eric@arcticfoxenergy.com
Sample Report: www.arcticfoxenergy.com/report