ENERGY MANAGEMENT & SUSTAINABILITY
IN TUNE WITH SUSTAINABILITY The Royal College of Music adopts machine learning to target net zero with the help of Energy Assets.
One of the world’s foremost institutes for musical excellence is adopting machine learning to help orchestrate sustainable building performance.
The Royal College of Music (RCM), ranked the number one global institution for performing arts, is harnessing the power of artificial intelligence (AI) to bring energy efficiency into harmony with plans to achieve net zero carbon by 2035.
The RCM estate includes a patchwork of buildings ranging from ultra-modern performance and teaching halls to Grade II listed premises dating from the 19th century. Its campus has been transformed in recent years, with a near doubling of the estate’s footprint, thanks to the addition of a new performance hall, a new performance studio, a large café and courtyard area, and a new interactive museum.
Fine-tuning energy performance for
Net Zero To support its net zero carbon journey, the institution is now employing AMR DNA machine learning from Energy Assets, across its Prince Consort Road site, which comprises six buildings of varying configuration and age. The tool will be analysing half hourly 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).
AMR DNA already provides data analytics on gas consumption to the RCM via The Energy Consortium, a specialist procurer of low carbon energy services for higher education institutes. Now though, the RCM will be using the tool for its Prince Consort Road buildings to drill down deeper into consumption routines and behaviours. This will deliver data in granular detail to support the institution’s broader Net Zero ambitions.
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“We have set ourselves the challenge to become net zero carbon by 2035,” says Gethin Lewis, Estates Projects & Environmental Coordinator at RCM. “To date, we’ve made good progress and by 2020 we reduced our Scope 1 and 2 emissions (gas and electricity) by 60% over the Higher Education Funding Council for England 2004/5 baseline target of 42% by 2020, even while our estate grew.”
This was achieved through actions such as upgrading lighting to LED fixtures, optimising energy control via a BMS, improving building thermal insulation and using boilers more efficiently.
An ensemble for a sustainable future “Now though, we need to go further,” adds Gethin. “While much of the focus will be on reducing Scope 3 emissions generated largely by services procurement, travel and transport, there is also more to do to cut energy-based emissions. Here, using the 2004/05 baseline, we aim to reduce our gas and electricity carbon emissions by 73% by 2027 and by 100% by 2035.”
In the case of gas, this means moving to lower carbon heat sources including heat pumps and point-of-use water heaters.
“For electricity, we’ll be looking at voltage optimisation, air conditioning system rationalisation, lighting replacement and IT infrastructure efficiencies. This includes adopting machine learning in Prince Consort Road to understand how AI can help us spot waste and inform actions leading to optimal energy performance.”
The analytics engine inherent in AMR DNA from Energy Assets, powered by kWIQly, has several report modes to save energy managers’ time. These include establishing achievable targets by KPI and weather condition for each meter, forecasting future use and identifying where changes have taken place; and diagnosing priority and cause of savings opportunities.
www.energyassets.co.uk/service/amr-dna twitter.com/TomorrowsFM
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