As the nations of the United Kingdom enter differing forms of lockdown in response to COVID-19, energy managers are facing the challenge of optimising their building portfolio efficiency and procurement strategies without knowing what the future holds.
Increasingly, real estate professionals are turning to Artificial Intelligence (AI) and advanced metering technologies to use historical data as a means of predicting the future by mapping multiple energy consumption scenarios that reflect the shifting sands of the new ‘abnormal’.
As industrial and commercial metering specialists we are working closely with energy managers to turn half-hourly gas and electricity data delivered automatically by advanced meters into benchmark performance thresholds that can inform better control over energy performance in buildings whatever the prevailing operating conditions dictated the pandemic.
The availability of energy monitoring and reporting portals coupled with innovations in artificial intelligence (AI) is helping managers to cut through huge swathes of data to bring clarity to decision-making.
For example, through our AMR DNA service we are enabling facilities managers in multiple sectors, including higher education, retail, local authorities and commerce, to use AI to develop accurate scenario planning linked to the status of the pandemic.
This means managers can adapt quickly to changes in COVID rules and be confident that whether it’s a full lockdown, localised tiered restrictions, a ‘COVID-secure’ normal requiring social distancing, or a return to pre-COVID normality, they are in a better place to maintain optimal energy efficiency.
“Through AI-informed scenario planning and reporting, energy managers can apply the correct measures to any given situation in order to minimise energy waste and optimise energy procurement,” said George Catto, Client Services Director at AMR DNA.
In scenario planning, we use kWIQly AI architecture to assimilate two years’ worth of half-hourly gas and electricity meter data into a performance model that can be measured against key criteria to identify waste. Energy waste can result from something as simple as leaving lighting on overnight or, more critically, failing to revise heating schedules when the clocks go back or forward.
Identifying and eradicating this waste can be a particularly pressing challenge in an environment of change, when building occupancy levels are fluctuating.
AI has the power to find and flag up areas for energy efficiency improvement because the system progressively ‘learns’ what best performance looks like. It’s a process that would take an army of analysts years to complete.
From this benchmark, energy managers can use the AI-driven model to develop building profiles that fit multiple pandemic scenarios. This approach has been adopted by retailers, local authorities, office occupiers and universities across the country to optimise energy efficiency and sharpen procurement strategies in these most uncertain of times.
Says George Catto: “Managing energy efficiency boils down to doing only what is necessary at least cost. Since Covid-19, ‘what is necessary’ has changed, as have metrics of cost. Pre-pandemic it was enough to measure and predict kWh in terms of normal operations and interpret cost around carbon impact and money.
“Rarely was health considered a ‘cost factor’. However, requirements to keep a building healthy have changed, and not in one dimension. Today, COVID means that energy and facilities managers need to take account of ideal temperatures, air-change rates, humidity, fresh-air circulation, heat recovery strategy and even comfort. Why comfort? - because breathing through PPE changes ambient requirements.”
The challenge to customise energy management is all the more pressing because of the dynamic situation Britain finds itself in as a result of the latest surge.
While AI cannot predict the economic well-being of an organisation, political sentiment or price of energy, what it can do is provide predictability using rock-solid data from historic meter-readings married with variables such as weather, occupancy, operational policy etc.
This enables AI to remodel outcomes under any set of assumptions based on available data.
“What is quickly becoming apparent is that COVID is challenging our conventional understanding of energy management and how meter data can positively affect scenario planning,” says George Catto. “This ability to forecast what the future can look like, and adapt quickly to changing circumstances, is gaining a lot of interest because no organisation can afford to burn money unnecessarily in today’s trading climate.
“Being able to implement new scenario strategies quickly also gives managers the ability to revise their energy buying strategies with confidence, because they have a clearer understanding of their future requirements based on data-driven insights rather than guesswork.”
AI is just one example of how digitalisation through advanced metering and data analytics is helping businesses to take control of energy performance in buildings, bear down on cost and reduce their carbon footprint.
Performance monitoring and analytics is available through web portals such as WebAnalyser, one of our platforms, which enables managers to create customised reports linked to half-hourly gas, electricity and water consumption data.
This platform includes the ability to set alarms to flag deviation from defined consumption parameters, and to rank and compare site efficiency and carbon performance vs benchmarks over defined periods. It is also possible to model the impact of renewables on carbon emissions and filter building reporting by footprint area.
It is also an important monitoring tool in sub-metering settings, enabling building owners to apportion precise costs for energy usage by tenant, rather than applying blunt consumption formulae.
So, looking ahead, it is possible that this awful pandemic will leave a legacy in which metering and monitoring, coupled with AI and data analytics, will be applied more systematically to optimise building energy efficiency and in doing so become a key contributor to Britain’s industrial and commercial sustainability goals.
Covid Scenario Planning for Energy in Buildings
Some buildings have always been intended for continuous occupancy, so it is not surprising that not all buildings respond to being unoccupied in the same way. However, given any set of buildings (and an energy management team) some patterns or norms emerge when a lock-down strikes.
While there may be an organisational delay, buildings that can readily be put into some sort of ‘low occupancy’ state will see a change in consumption. This raises questions such as: What is achievable in terms of energy reduction, is the optimal state applicable and uniform across an entire estate, where can it be improved, how will the estate respond to increased ventilation under colder external conditions and how can energy purchasing decisions best reflect the different scenarios?
What is needed is a strategic approach. These graphics illustrate the type of analysis that can help.
This chart represents a set of buildings belonging to one owner where each dot is a gas meter. Some buildings have been switched off entirely (bottom line).
While it can be seen that one very large building (top-right) and a handful of others have actually increased consumption, the majority have seen a 20%-40% reduction over normal operating conditions, depending on the level of turndown. Having this knowledge means that managers can assess opportunities for additional energy saving measures.
This chart shows on top a weather adjusted model by time of week and outside temperature (from which daily profile averages given forecast temperature can be calculated).
The section below shows the single daily profile of actual consumption (blue bars) compared with achievable (gray shaded). This immediately illustrates a problem; that although consumption inside occupancy (core hours) has improved, during evenings and before occupancy the consumption has risen.
Having this information to hand, enable energy consumption under different scenarios across portfolios to be accurately tracked automatically, to identify not just the impact of turning down but also where buildings are not responding as anticipated. As well as enabling follow-up action, this data can greatly assist with budgetary and forecast scenario planning.