Analysis of an anonymized subset of Gridium building data benchmarks the impact of the coronavirus pandemic.
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Join Gridium’s Director of Data Science, Greg Anderson, for a discussion of our new COVID-19 building operations index analysis.
Topics include the distribution of our use index values across a dozen building types, the relationship between peak demand reduction and the C-19 use index, an analysis of any effects from Energy Star scores, and changes to scheduled load versus overnight baseload.
This analysis is run on an anonymized subset of Gridium building energy data spanning nearly 700 electric meters. For each meter included, at least one year of electricity interval data had to be available, the trailing 12 month total energy use had to be at least 500,000 kWh, and only a building’s main or totalized meters were included. We set the start of the COVID-19 period for building operations at March 9th, 2020. When our analysis makes a comparison to actual energy use during the C-19 period to an “expected” level, the expected level is calculated using actual weather data for the period.
Buildings are classified into one of 10 types: Biotech, Corporate, Hospitality, Industrial, Medical or Medical Office, Multi-Tenant, Municipal, Residential, Retail, or School. An 11th group, Office, is composed of both Multi-Tenant and Corporate buildings. The data is also grouped into one of three regions; Northern California, Southern California, and Ex-California.
The key metric used for this analysis is a “use index”, which is simply the ratio of actual energy use to expected energy use multiplied by 100. Generally this is calculated by first summing expected and actual use to the reporting time period (e.g. daily or the entire post-COVID-19 period) by building, then calculating the use index for each building-time period. Other variations are made as needed.
A secondary metric is a “peak index”, which compares actual to expected peak demand for the full calendar months during the COVID-19 period. Actual peak demand is a maximum interval demand value, so comparing it to a model estimate (which smoothes out extreme values) would tend to underestimate the true extent of demand reduction. Instead, we used a temperature-adjusted actual peak demand calculation to better preserve the variability of actual maximum demand values.
Register for the webinar and receive over email the index analysis Factbook with all its charts and graphs. If you need to see an analysis of your building’s data, a free trial of our analytics is a good place start–please get in touch.
