MONITORING & METERING


Have you developed an energy consumption hierarchy?


There’s probably never been a more important time for industry to get ‘energy fit’, both to bear down on cost and to contribute to the UK’s wider sustainability goals.


David Sing


Managing director, metering and data, Energy Assets


www.energyassets.co.uk I


t’s fair to say that there have been some mixed messages from government of late about the roadmap to net zero, but the ultimate


aim remains to reach this objective by 2050. This target is enshrined in law, so what does it mean for industrial and commercial enterprises? Aside from increasing pressure


from customers, stakeholders, and suppliers to demonstrate their sustainability credentials, it’s also likely that businesses will, in time, be subject to additional reporting regimes measuring their carbon footprint and demonstrating progress. So where to start? At a high level,


there are some freely available tools to help businesses assess their greenhouse gas emissions, such as the Business Carbon Calculator, because like every improvement process, defining the start point is the first critical step. However, what’s already clear for most organisations is that their carbon output will be largely determined by their energy consumption. As a result, energy managers have


a critical role in influencing progress towards net zero - and many are turning to advanced metering technologies, consumption analytics and machine learning to make a difference. Of course, monitoring and measuring


energy usage is already a well- established and routine business function, with automatic monitoring and targeting (aM&T) systems widely in use to collect and report on meter data to identify trends and unusual spikes. What’s changing is the emergence of machine learning, informed by artificial intelligence (AI), capable of turning years’ worth of half-hourly gas and electricity meter reads into value- adding, granular energy consumption data for entire building portfolios.


Develop a metering strategy Whatever the analytical tools adopted by companies, core to energy efficiency progress is an automated meter reading (AMR) system and a


EIBI | FEBRUARY 2024


metering strategy. One area often overlooked in energy optimisation is sub-metering. Despite the sophistication of aM&T systems, it’s still surprising how many industrial and commercial settings overlook the value that electricity sub-metering can deliver in better understanding consumption…and saving money. For larger single site operations, such


as offices, industrial plants, or hospitals, sub-metering 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 (ESOS). In multi-occupancy settings, such


as retail centres, service stations or transport hubs, sub-metering enables businesses to monitor their energy usage much more 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 much greater clarity over the make-up of their consumption profiles. This type of platform helps make


sense of half-hourly data delivered via AMR systems 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. 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


Sub-metered data can feed into an aM&T dashboard for clarity on consumption


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%.


AI and machine learning Now though machine learning, informed by AI, is enabling energy managers to go even further, dive deeper into data and apply dynamic learnings to optimise building performance. The core data informing these models come from historic half hourly meter readings. Analysing such data manually would require an army of analysts – but with the Energy Assets AMR DNA machine learning system, it’s possible to assimilate two years’ worth


of half-hourly gas and electricity meter data quickly and to create an energy performance model that can identify waste. For example, to support its net zero


carbon journey, the Royal College of Music is employing AMR DNA across its Prince Consort Road site, which comprises six buildings of varying configuration and age. The tool will be analysing meter data from a new network of electricity sub-meters to create consumption patterns that can spot tell-tale signs of energy waste, such as equipment running needlessly, or heating controls incorrectly set. This AI-informed system will


progressively learn what benchmark performance looks like, fine-tuning the effectiveness of the established building management system (BMS). It can also take account of variables such as weather, occupancy level and operational patterns. This capability enables energy managers to model multiple energy performance scenarios using rock-solid data.


Energy consumption hierarchy You’ve got the data, so where next? Machine learning tools can help create a consumption hierarchy to prioritise the actions that will deliver the fastest payback, whether on cost or for carbon reduction. For example, government research suggests that a reduction of 1% in average heating temperatures can lead to around an 8% cost saving. Any successful plan will likely include a mix of practical steps, such as adjusting and tailoring heating to match occupancy levels, installing low-energy lighting, etc, along with behavioural change. One Energy Assets customer uses operational data to drive its ESOS actions and measure the contribution to efficiency of its investment in renewable energy. At the same time, staff engagement around energy efficiency is encouraged through a culture of shared ownership and individual responsibility. This includes nominating an Energy Champion to undertake a daily energy walk to help to eradicate waste, identify inefficient equipment usage and flag poor energy habits. Often, it’s a question of spotting


improvement opportunities hiding in plain sight, but sometimes these can be the hardest to identify because they are ingrained in a building’s legacy performance and can be easily overlooked. However, with granular energy


performance data, made possible by effective metering, automatic monitoring and advanced analytics, managers now have the information at their fingertips to optimise energy performance across their entire building portfolio. ■


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