Building Data
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      1. Station A Knowledge Base
      2. Modeling
      3. Building Data

      How accurate is Station A’s building analysis?

      The building data estimates provided through Portfolio View are indicative and intended to provide a framework to kickstart your analysis. Inputs can be easily updated with actuals during a complimentary data review with our team.

      How we generate our initial building data estimates

      Your Portfolio View begins with a list of addresses. Once your list of addresses is uploaded, our platform:

      • Matches each address to an installable area at that address, usually an identifiable roof.
      • Predicts each building’s energy use based on the building size.
      • Estimates the cost of electricity based on the most likely tariff at that location.
      • Applies the latest build costs and incentives including the latest ITC for solar and other clean energy options in the area.

      After this we generate indicative financing and environmental impact estimates for building a solar or clean energy project at the addresses provided. Portfolio View can quickly process any amount of buildings, whether your portfolio is a single building or thousands. See further details below.

      The accuracy of the initial building data estimates displayed in your Portfolio View depends on:

      1. Our ability to match the addresses to actual locations.
      2. If the standard assumptions we make about the use of your building are correct.
      These can be easily updated with actuals during a complimentary data review with our team. Book your data review with our team here

      How to refine your building data estimates with actuals

      The indicative estimates provided for each building in your Portfolio View can be further refined during your complimentary data review with our team. Adding your building's actual data will instantly re-run your portfolio and increase the accuracy of your estimates. Sample inputs that can be refined to increase the accuracy of your estimates include: 

      • Roof area (sq/ft)
      • Annual energy consumption (kWh)
      • Cost of electricity ($/kWh)
      • Annual peak demand (kW)

      Building data analysis details

      Here are the steps we go through to analyze each building address:

      1. We use Google to geocode the addresses, so we have a reliable point in the US to match your address.
      2. Using large datasets of buildings and parcels in the US, we look to see where your geocoded location lies and match it with the closest building.
      3. We take the matched building (and any height information) and compute the roof area and floor area for your building.
      4. We use the size of the building along with the climate zone of where the building is located to estimate the energy use of the building (these estimates can be easily adjusted during a complimentary data review with our team).
      5. We similarly estimate the annual peak load for your building by using the data to make assumptions on the building use type, together with the size of the building and climate zone.
      6. We look up the tariffs for the cost of energy in your area based on zip code and match you to the most likely tariff.
      7. We then take the roof area and recommend a solar system for your roof. We make some assumptions such as 70% available roof area coverage. By default, we do not oversize your system if your annual usage is estimated to be low. If your roof size is large and you want to build as much as possible, this estimate can be refined easily. We also right-size the system to accommodate local net metering and billing policies such as CA NEM3.
      8. We estimate build costs for your recommended solar system, based on recent solar build costs in that region and system size.
      9. We estimate the annual savings on your utility bill based on the annual solar system output and your cost of electricity.
      10. We factor in any major incentives in your area, including the ITC but also SRECs where applicable. We recompute the effective cost of your system based on these incentives.
      11. Our software then calculates a number of financial metrics for your available clean energy options. We calculate the financials for an all-cash deal, where you pay for the system upfront, as well as options like power purchase agreements (PPA).
      12. For a PPA, we predict the PPA rate that you can achieve based on your system size, peak demand, annual usage, and an assumed term of 20 years. We then predict the savings you would see based on that PPA rate and your current cost of electricity.
      13. In addition to the options listed above we also provide analysis on EV Charging, Community Solar, and other clean energy options for your site.