NYSERDA PROPTECH CHALLENGE 2021
COVID-19's Impact on Tenant Energy Use
What factors go into accurately predicting energy consumption as building occupancy changes?
Learn more about:
- What utiliVisor's take on tenant energy data modeling really means for NYC commercial buildings
- The exact steps utiliVisor executed to complete the PropTech Challenge
- Why utiliVisor's proven submetering practices consistently deliver energy and cost savings
COVID + Forecasting Energy Usage
What is the PropTech Challenge?
The Tenant Energy Data category is designed to improve understanding of the drivers of electricity consumption within sub-metered office space in NYC. The purpose of this effort is to identify new modeling assumptions that account for COVID-19’s impact on occupancy levels. This category provides real world consumption data from a single large office tenant in Midtown Manhattan along with some building-wide data.
– NYSERDA PropTech Challenge
utiliVisor has been working with NYSERDA for years and has applied our services to many projects throughout NYC and the country with great success. Bringing attention to the importance of submetering data to drive energy awareness and solutions is well within our capabilities and we are glad the industry is highlighting the importance of this.
Data scrubbing and refinement
Use data to develop predictive formulas and algorithms
Analyze the results
and adjust accordingly
Display in a platform that is digestible and actionable
See how we responded
Paul Gutekunst 0:07
Hello and welcome to utiliVisor's response to the NYSERDA PropTech Challenge. My name is Paul Gutekunst and I am a lead development engineer here at utiliVisor.
Paul Gutekunst 0:15
The challenge is about understanding how COVID-19 impacted building occupancy levels and tenant energy usage then to determine if there's any correlation between the two. Finally, we want to identify factors that go into accurately predicting tenant energy usage.
Paul Gutekunst 0:33
We broke down our approach down into four steps. First, we scrub and refine the data that is provided. Second, the data is used to develop predictive formulas and algorithms then analyze the results and adjust accordingly. Finally, our results are displayed on a platform that is easy to digest and actionable.
Paul Gutekunst 0:52
To take a deeper dive into how utiliVisor scrubs data, we import the raw data into utiliVisor's suite of analytics tools that will automatically normalize all timestamps and apply null field techniques. From there we can learn more about the data patterns and formulate assumptions on what this meter data is; using utiliVisor's data history and experience.
Paul Gutekunst 1:12
The scrub data is presented to the operations team where it can be visualized inside utiliVisor's platform again. The operations team will analyze the data and get a basic ideas such as occupancy levels, business hours, holiday schedule, and even when the cleaning crew arrives. Then the data is fed into statistical analysis tools that is partitioned by hour of day, day of week, month of year, and season, that will help identify outliers and visualize the data within the model.
Paul Gutekunst 1:39
The result is we can now confidently answer questions the NYSERDA PropTech Challenge presents.
Paul Gutekunst 1:46
You can visit our website to view our forecasts of consumption for all tenant usage meters on August 31, 2020.
Paul Gutekunst 1:54
So how correlated are building-wide occupancy and tenant consumption? From our analysis there is at least some. This correlation was dependent on tenant meters with notable increases in power during business hours.
Paul Gutekunst 2:08
We used R squared to measure the quality of our predictive model, we reached up to point seven nine r squared in our current prediction model.
Paul Gutekunst 2:17
So what predictors were most important in determining energy efficiency? From a tenant perspective, using time of day, day of week, building occupancy and outside air temperature, helped build our predictive models. From a building perspective, in early June 2020, as the building reach higher outside temperature, the ConEd Utilities steam consumers were shut down and had drastic impacts on building energy efficiency.
Paul Gutekunst 2:42
So which day has had the most energy efficient occupancy level? Pre COVID, this was on March 10, 2020. During COVID, the best day we had was on August 17, 2020.
Paul Gutekunst 2:53
What else can we conclude from our model? Well, we can see what the base load is for the building independent meters before and during COVID. We can see that the office cleaning crew was visiting the tenant space later in the day between nine and 11pm. Finally, we can get an idea of the tenant occupancy times based on the consumption data.
Paul Gutekunst 3:11
And last but not least, what would we need to better our current prediction model, the bigger items we would like is building and tenant square footage. Also, knowing the tenant type would have been useful whether it be an office building tenant, restaurant or server farm. And finally having tenant occupancy count by hour instead of building occupancy totals would help improve our models.
Paul Gutekunst 3:31
We enjoyed what we learned throughout this process. If you have any questions or comments, please feel free to contact us by visiting www.utilivisor.com.
Questions and answers
utiliVisor's solutions for NYSERDA's PropTech Challenge problem statements.
Click here to request the download and view our forecasted consumption for all meters on August 31st, 2020.
There is some. This correlation was dependent on tenant meters with notable increases in power Monday-Friday, 7:00 AM – 7:00 PM. Meter 1-004, for example, utilized total occupancy to capture the higher consumption of the tenant pre-COVID.
- We reached up to 0.79 R^2 in our current prediction model.
- utiliVisor utilized the R-Square value of each prediction to determine a good fit (greater than 0.75 R^2).
- We also used probability functions to determine the impact of each nominal value for every individual model.
- utiliVisor used historic time of day, day of week, building occupancy, and outside air temperature to build our predictive models.
- As the building reached higher OATs (6/2/2020) there was a Steam ConEd meter shutdown of Heating Steam. This minimized simultaneous heating/cooling that took place.
utiliVisor converted the ConEd utility electric and steam meter data to the same metric (MBTU/Occupant) to measure efficiency. The building operated at 0.182 MBTU/Occupant on 3/10/2020 (pre-COVID). During COVID and after shutting down major equipment such as the ConEd utility steam consumers around early June 2020, the most efficient building operation was on 8/17/2020 at 0.383 MBTU/Occupant.
- Base Tenant consumption, plug loads… etc. (COVID April/May 2020)
- Base HVAC consumption to keep space setpoint without occupants (COVID July/August 2020)
- Office cleaning occurs around 9:00 -11:00 PM
- Actual tenant occupancy times.
- Increased energy consumption as a function of occupancy
- Building and tenant square footage.
- Tenant type (office building, restaurant, server farm)
- Tenant occupancy count by hour.
- HVAC type
- Tenant holiday schedule
- Meter type
- Meter calibration sheets
Why tenant energy data modeling benefits NYC commercial buildings, especially with the upcoming NYC Local Laws
With the upcoming NYC Local Law 97, building owners need solutions that make sense for their specific situations.
utiliVisor helps building owners understand the data and metrics and provides that information in an understandable format for tenants. Driving change requires direct, succinct communication and showing measured results to building tenants and owners, so they avoid the penalties associated with non-compliance.
How utiliVisor’s predictive modeling and data interpretation delivers energy and cost savings
utiliVisor’s strength is that we eliminate drowning in data seen with many dashboards and analytics programs. utiliVisor focuses on the facts. Our Operations Center and energy analysts use our technology, analytics, and 40+ years of experience to interpret data for the customers and present solutions that will drive actual savings. We discover, document, and track savings and energy efficiencies.