David Sing, Group Managing Director (Assets), discusses energy management and the measure of sustainability.
One of the recurring themes in plans for recovery from the coronavirus pandemic has been a determination in government circles to invest in a greener, more sustainable future.
We are all only too aware of the tragic human cost and economic turmoil wreaked by the crisis, but one unexpected consequence of the new way of working has been lower carbon emissions. The government, in the words of the Prime Minister, is looking to ‘entrench those gains’ through the recovery phase on the road to net-zero emissions.
At the start of this year, no commentator would have predicted an 8% drop in CO2 emissions for 2020, but latest research shows this is likely to be the case. And in May, National Grid confirmed a full month without any input from the country's coal-fired power stations, with low carbon energy sources providing around 70 per cent of the UK's power during this period.
Of course, these are exceptional times, but with promised investment in a more sustainable economy, the question remains what can we all do to influence the future?
One of the key contributors will be improved energy efficiency across households and particularly in industrial and commercial settings. The context for advanced metering and intuitive data analytics is climate change and the government’s longer-term net zero carbon emissions commitment, but for businesses and public sector organisations, taking control of energy drives an immediate and clear financial gain alongside the environmental benefit.
At Energy Assets, we are helping businesses to embrace digitalisation in energy metering, monitoring and analytics to crunch vast amounts of consumption data to take control of energy performance in buildings and reduce their carbon footprint.
To date, one of the biggest challenges facing energy managers has been making sense of the sheer volume of half-hourly consumption data delivered automatically by advanced meters. Now though, through energy monitoring portals and the application of artificial intelligence (AI) tools, managers can cut through this information overload to bring clarity where there was fog and automated action where there was human interpretation.
Performance monitoring and analytics is available through web portals such as WebAnalyser, which enables managers to create customised reports linked to half-hourly data delivered by gas, electricity and water meters.
This includes the ability to set alarms that will flag deviation from defined consumption parameters, rank and compare site efficiency and carbon performance vs benchmarks over defined periods, model the impact of renewables on carbon emissions and filter building reporting by footprint area.
These alerts can be linked to consumption profiles to flag up performance that is over or under a given threshold and interfaced with KPIs to monitor, for example, energy usage per square meter, with results automatically emailed to managers to communicate ranking and any issues. All of which can be delivered via a customisable dashboard with an ability to dive deep into data down to an individual meter point.
WebAnalyser can also be used in sub-metering settings, enabling building owners to apportion precise costs for energy to tenanted occupiers, rather than applying blunt consumption formulae.
Digitalisation is also ushering in a new era of artificial intelligence capable of modelling data to drive optimal energy efficiency.
For example, our AI data analytics platform – AMR DNA - is enabling complex, multisite organisations, such as retailers, local authorities and universities to analyse years of half hourly consumption data and identify patterns of energy waste that it would take an army of analysts years to find.
In the case of one local authority, it took just 15 minutes to identify waste valued at £25,000 using this methodology. In another project, the Central England Co-operative generated a 206% return on investment by identifying and eradicating energy waste and implementing evidence-based efficiency strategies.
The core value of this AI system is its ability to assimilate meter data into a performance model and measure this profile against key criteria to identify event exceptions, which could be as simple as leaving lighting on overnight or, more critically, failing to adapt heating schedules to a change to British Summer Time. Over time, the AI platform ‘learns’ what best performance looks like for each building and produces a ‘to-do’ list to optimise efficiency.
It is by combining automated meter reading with advanced monitoring and AI that energy managers can spot the trends and actions that will inform the best ways to save energy, because self-evidently the only way to accurately understand energy consumption and building efficiency is to look at data historically and apply it to models for the future.
With all these tools in their armoury, organisations are in better shape than ever before to bear down on energy costs and contribute positively to the wider climate challenge.
At the same time, these increasingly accurate datasets can help contribute to a better understanding of the wider energy network capacity requirements as the country moves towards greater electrification and a low carbon economy. Understanding what is needed to support investment in infrastructure such as EV charging networks, demand side management, ground source heat pumps, peaking plant supply, renewables and battery storage is key in assessing the need for network reinforcement.
More than ever in the future, metering and monitoring, coupled with AI and data analytics, will be the measure of our success in meeting our energy efficiency and sustainability goals.