Trial / Demo
Recently, IES ran a webinar with experts from WSP and RYBKA, covering how you can elevate your operational building data analysis skills, whilst equipping yourself with the tools and insights needed to transform the way you manage building performance data.
During the Q&A portion of the session, common themes emerged within the questions asked. Read on for the answers to these questions, and to learn more about how iSCAN can streamline operational data analysis, saving you time and money.
iSCAN is designed as a cloud-based platform specifically for centralising and analysing time series data, including actual energy consumption from sources like utility meters, BMS (Building Management Systems), IoT sensors, and historical files. You can upload data via CSV imports, direct API connections (e.g., OBIX or MQTT protocols), or automated pulls from utility portals. This allows for granular analysis down to 1 minute resolution if needed. Once uploaded, iSCAN can handle data cleaning, gap-filling and tagging for easy organisation.
Yes, iSCAN supports translating energy usage data into CO2 emissions estimates to support carbon reporting and evaluation of reduction strategies. While iSCAN itself focuses on data aggregation and operational analytics, it integrates seamlessly with the broader IES ecosystem (IESVE) for emissions calculations.
However, even within iSCAN just as a standalone software, you can apply emission factors (e.g. grid-specific CO2e factors from .gov or custom hourly profiles) directly to your uploaded consumption data via the expressions tool. You can then benchmark in iSCAN to compare how your building compares to one of a similar building type and area. It allows tracking operational emissions, benchmarking against targets, and simulating reduction scenarios such as retrofit impacts or efficiency measures to quantify savings.
For more advanced whole life carbon modelling, linking your real building to IESVE/One Click LCA allows for detailed simulations – find out more.
iSCAN doesn't have fully built-in, one click calculators for these exact statistical metrics (RMSE, CV(RMSE), R²) as standard, but you can achieve them directly through its customisable expressions features.
iSCAN's Python API lets you automate calculations on your actual vs. simulated data e.g. pulling time series from uploads or linked IESVE models to compute RMSE (root mean square error) for prediction accuracy, CV(RMSE) for normalized error variation, or R² for statistical goodness of fit. This is useful for performance gap analysis, where you compare metered consumption against calibrated models.
Find out more about Python Scripting with IES here.
Yes, iSCAN does have a feature to generate alarms for operational faults, as part of its Alert functionality. It automatically notifies users via email when issues arise, such as systems operating outside expected schedules or thresholds (e.g. out of hours usage or performance deviations).
You can set up automated rules via Project > Rule Libraries page, with dedicated "Alarms" designed specifically for fault detection and alerting (see below image). These integrate with reporting tools to deliver client notifications on things like faults or anomalies, helping close the performance gap between design and real world operation.
Was the occupancy variation monitored as well to get an accurate assessment of how the building is being used?
The occupancy of each individual classroom is not being monitored, but the overall school role is being tracked to see how it is being utilised. We’ll also be accessing classroom utilisation data in terms of when the classes are used and how much they are used throughout the year.
One of the issues with the way this POE works is that the data is in a 4-6 week delay, so abnormalities can’t be tracked live or more regularly. We’re trying to set up a link with the MVHR units so we can get the data on a live or near live basis.
There’s a nominal EUI target of 83kWh/m2/year, excluding swimming pool and community use, but this was not the target at design stage, it’s more of an ambition.
Various projects have been carried out to calibrate IESVE models with iSCAN. One such project was carried out by SSE Enterprise Energy Solutions, who created a calibrated model with 99.3% accuracy to predict the savings potential of a unique heat recovery air conditioning strategy.
IES also developed a calibrated model, or Performance Digital Twin, of Keppel Bay Tower in Singapore. The model was calibrated with multiple data sources onsite to ensure it was over 99% accurate, before being used to identify and virtually test the impact of a range of viable energy conservation measures (ECMs).
The energy model used within IES Consulting’s work on a high-end refurbishment project in Dublin was successfully calibrated with a 1.0% variation (Mean Bias Error-MBE) between the actual building metered energy and the model energy usage. Additional opportunities for energy savings were then found and recommended to the client to improve building performance.
We know getting started with new technology can be challenging - that's why IES offers have free on-demand training, paid online training or even bespoke training options to upskill your teams in operational data analytics and iSCAN.
Visit our webpage, or sign up for a trial today.
