Energy Tracking in Alberta
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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.
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5th International Conference : Adam Hawkes
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Meniscus Advanced Energy Analytics Platform
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Green Button for America Initiative Overview
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
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如果您处于以下几种情况：
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Energy Tracking in Alberta
- 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
- 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
- 32. Historical Comparisons 3y Min 3y Max 3y Average Usage
- 34. Summaries
- 35. Thank you! Questions? Eric Hennessey eric@arcticfoxenergy.com Sample Report: www.arcticfoxenergy.com/report
Editor's Notes
- We need 6 different files to obtain the complete energy picture