BSEE-MAY21-P36 Energy Management_Layout 1 16/04/2021 10:57 Page 36


BSEE


ENERGY MANAGEMENT


Optimising Energy Consumption in the New


‘Abnormal’


David Sing Group Managing Director Energy Assets


Lockdown restrictions are easing, but there is still uncertainty among businesses about what future working patterns will look like. For energy managers this creates a unique challenge ­ how best to prepare their building portfolio for occupancy levels that may look very different in what can best be


described as the new ‘abnormal’.


hey need flexibility to adapt their energy profiles to what will likely be a progressive return to the office or, in the case of retail, a return to physical trading. So, energy professionals are leveraging the value of data through metering technologies, consumption analytics and artificial intelligence (AI) to help them prepare for an uncertain future. Historically, many industrial and commercial organisations have used automatic monitoring and targeting (aM&T) systems to collect and report on data to identify consumption trends. Now though, thanks to the pandemic, an increasing number are also looking to artificial intelligence to turn years’ worth of half-hourly gas and electricity meter reads into energy consumption models that can reflect different operating conditions.


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Whatever the tools at their disposal, the start point for every organisation is automated meter reading (AMR) technology and a metering strategy that could involve sub-metering. This will deliver data – which is the currency of energy efficiency and the measure of sustainability.


David Sing, Group Managing Director (Assets) at Energy Assets, a metering services and energy data company, says that leveraging the value of data through monitoring, analytics and AI can transform our understanding of energy efficiency. “With these digital tools in their armoury, organisations are in a better shape than ever before to bear down on energy consumption, to shape their efficiency strategies and to contribute to the nation’s wider sustainability challenges on the journey to Net Zero,” he said.


“The value of AI and analytics when applied to consumption datasets will become increasingly evident as businesses adapt to a future characterised by greater electrification, the integration of renewables and innovations in demand response.”


David says that one area often overlooked in energy optimsation strategies is sub-metering. “Despite the sophistication of aM&T systems, it’s still surprising how many industrial and commercial settings overlook the value of smart sub-metering. Whether it’s electricity or water, sub-metering can be an incredibly valuable way to better understand utilities consumption…and save money.


“For larger single site operations – such as offices, industrial plants or hospitals - sub-metering


36 BUILDING SERVICES & ENVIRONMENTAL ENGINEER MAY 2021


provides the ability to monitor energy usage by floorplate or function. It also enables the collection of data linked to carbon reduction obligations and the Energy Savings Opportunities Scheme.


“In multi-occupancy settings, such as retail centres, service stations or transport hubs, sub- metering enables businesses to monitor their energy usage accurately – and make positive changes - rather than being charged on a broader measure such as footprint.” Sub-metered data can feed into an aM&T dashboard, such as Energy Assets’ WebAnalyser, alongside master meter readings, giving organisations clarity over their consumption profiles.


This type of platform helps make sense of half- hourly data delivered via AMR meters and enables organisations to identify ways to reduce energy costs, pinpoint energy wastage and flag up unexpectedly high consumption. Bespoke reports can also be tailored to single site or multi- site operations, with alerts set to highlight consumption patterns outside set parameters. Where new SMETS2 meters are fitted in micro businesses, such as pubs and small retail outlets, the data can be received using a Consumer Access Device (CAD). A CAD simply connects to the Home Area Network (HAN) and once connected can deliver data to a remote server in near real time. This enables detailed energy consumption profiles to be developed and progressively monitored for efficiency. These monitoring systems can inform robust and long-term energy management practices that, according to the Department for Business Energy and Industrial Strategy (BEIS), can lead to average energy savings of 10-15%.


Now though, artificial intelligence is enabling energy managers to go even further, dive deeper into data and apply dynamic learnings to optimise building performance.


For example, through its unique AI driven data analytics tool, AMR DNA, Energy Assets is applying AI to enable organisations to map multiple energy consumption scenarios linked to different levels of building occupancy. This could become invaluable during the easing of lockdown. The core data informing these models come from historic half hourly meter readings. Analysing data on this scale manually would need an army of analysts – but with AI, it can be a matter of hours, even minutes to learn lessons from the past that can yield results for the present and the future.


This means managers can be confident that whether it’s a full lockdown, localised tiered restrictions, a ‘COVID-secure’ normal requiring social distancing, a return to pre-COVID ways of operating – or something in between - 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, bear down on costs associated with consumption and optimise energy procurement,” said George Catto, Client Services Director at AMR DNA.


In scenario planning, Energy Assets uses 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.


In the case of AMR DNA, AI finds and flags up areas for energy efficiency improvement because the system progressively ‘learns’ what best performance looks like.


While AI cannot predict the economic wellbeing of an organisation, political sentiment, or price of energy, what it can do is provide predictability using rock-solid data from historic automated meter readings, particularly when it takes account of variables such as weather, occupancy, operational policy etc.


This enables AI to remodel outcomes using multiple sets of assumptions linked to available data.


“What is quickly becoming apparent is that one legacy of the pandemic will be to challenge our conventional understanding of energy management systems,” says George Catto. “Being able to implement new scenario strategies quickly 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 historic assumptions.”


www.energyassets.co.uk Read the latest at: www.bsee.co.uk


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