We will give you a read-only login to Deep Energy.AI to view a baseline year against default market tariffs.
DeepEnergy provides like-day analysis, load variability disaggregation, 20-year cash-flow and much more!
Deep Energy AI relies upon source utility meter data. For electricity, this is NEM12 formatted data from the distribution network or meter data agent. While you can import CSV electricity data in eQuest format, we highly recommend the sourcing and use of the original metering data, as it has not been modified by the retailer for billing purposes. The biggest benefit of source data? Accurate calculation of electrical maximum demand.
For gas, this can be CSV interval data at the finest resolution possible (sometimes this is only daily totals!)
From there you can explore your energy data, tariffs and costs, export images or raw data samples and much more!
Artificial Intelligence and Machine Learning algorithms have grown in popularity for language models in recent years, despite AI+ML having been 'created' during the 1940s by Turing.
Using modern techniques, applied to 5-minute intervals, an enormous amount of value can be extracted from utility data alone.
Utility data is the 'source-of-truth' when it comes to finance, accounting, carbon reporting or certificate generation. It is also ubiquitous and standardised - if there is a meter, there is metering data.
This allows Virtual Energy Assessments to be conducted across a large volume of metering points and metering data, rapidly!
Connecting to BMS/SCADA is usually a lengthy process due to a lack of data or communications standards, or it being proprietary in nature. Detailed energy assessments can be initiated from the results of a Virtual Energy Assessment, allowing resources to be focused upon poorly performing assets. At this point, behind-the-meter data can be analysed for fine-tuning of the building by experts in the field.
A Virtual Energy Assessment can use a bottom-up (metering) or top-down (modelling) approach. We use a hybrid approach for existing buildings, and a top-down approach for new buildings.
We can adopt existing modelling outputs and mesh them with metering data to provide a synthesis of both.
While energy is a focus, we are experts in technology in the built environment.
Our experience provides us with a holistic perspective across different industries, typologies and use cases.
We engage our clients on a multitude of aspects of technology in buildings, with a principal focus on digitalisation, building optimisation and reporting through development of design briefs, review and markup of specifications and high-level integration of subsystems.
Deep Energy AI is currently in a closed beta. Contact us to request access and we'll get back to you shortly!