Energy consumption in non-domestic buildings can be monitored to varying levels of granularity, such as site, floor, room, and asset-level.
But increasing levels of granularity require the creation of new physical monitoring points.
However, many businesses refuse new physical monitoring points due to proximity to high voltage loads, security concerns, risk of disruption to business-as-usual, etc.
Every non-domestic building has, at least, a main meter in place to collect site-level energy consumption data for billing purposes.
But this site-level data is much too high-level to enable precise location of energy waste and loss.
However, many businesses cannot afford the large upfront capital cost and up to 8 years payback to permanently deploy electrical sub-meters to collect asset-level data.
Deployment of new energy metering infrastructure in new physical monitoring points usually requires the contracting of accredited Electricians.
But the resulting troves of streaming data require either on Data Scientist hires or Bureau Services contractors for data processing and analytics.
Electricians, Data Scientists and Bureau Services are costly to hire and unavailable on-demand, which can leads to significant work delays.
Our approach is to monitor energy consumption in non-domestic buildings at the asset-level, since this is precisely where energy waste and loss occurs.
Our proprietary machine learning model turns every load-bearing asset into a virtual monitoring point.
This makes every load-bearing asset in a building accessible for monitoring irrespective of proximity to high voltage loads or security concerns, and poses no risk of disruption to business-as-usual.
Our approach is to extract historical site-level energy consumption data from the existing metering infrastructure of a non-domestic building.
Our proprietary machine learning model thereafter disaggregates the site-level data to asset-level.
Businesses incur zero upfront capital cost while extracting incremental value existing metering infrastructure at zero marginal cost This eliminates cost and payback as barriers to adoption.
Remote data collection from old energy metering infrastructure and new virtual monitoring points is provided as a turnkey service by our team.
The resulting troves of collected data are automatically processed and analysed as a turnkey service by our proprietary data analytics engine.
Output analyses is automatically visualised and available on-demand as a turnkey service from the convenience of a self-serve web dashboard.
Our virtual monitoring points are faster to initially setup and easier to thereafter derive data from compared to conventional physical monitoring points because they do not require in-person access to every load-bearing asset on a site.
Our approach provides faster access to all points where energy waste and loss occur in any non-domestic building, without the health & safety risk of proximity to high voltage loads or security risk of breaching restricted areas or business disruption risk from (un)planned power cuts.
Our reliance on existing metering infrastructure for the site-level energy consumption data from which to derive the asset-level equivalent for all virtual monitoring points is significantly cheaper than buying new asset-level metering infrastructure.
Our approach provides affordable asset-level energy consumption data for in any non-domestic building, without the high upfront capital costs and up to eight years payback that have historically made affordability a barrier to adoption of asset-level energy consumption monitoring by businesses.
Our end-to-end services engine for data collection, processing, analysis, and visualisation is largely automated, which assures 24/7 availability relative to labour-intensive data services from accredited electricians, data scientists or Bureau Services.
Our approach provides round-the-clock availability of actionable insight into the precise location of points where energy waste and loss occur in any non-domestic building. This frees businesses to focus scarce human and capital resources on the highest impact energy reduction opportunities.