MONITORING & METERING
resources to bear down on energy consumption and reduce carbon emissions? At one level, this means optimising the
value of energy metering, consumption monitoring and data insights. At another it means applying advanced technologies, such as machine learning, artificial intelligence and automated monitoring to drive out waste. Getting all these systems working in harmony is critical to optimising energy efficiency
David Sing
“Often, it’s a question of spotting improvement opportunities hiding in plain sight, such as
equipment running needlessly or heating controls incorrectly set –and machine learning is the perfect tool to do that by crunching big data”
and reducing carbon emissions. For managers, this means:
• Capturing consumption data in granular detail via automated meter reading (AMR) systems.
• Monitoring and analysing data through advanced AM&T portals, such as WebAnalyser, and setting automated alerts for unusual
HEAVY DATA LIFTING WITH DIGITAL SYSTEMS Machine learning, such as that employed by AMR DNA from Energy Assets, uses artificial intelligence (AI) to automatically learn about and improve energy consumption. It does this by assimilating half-hourly meter
patterns of behaviour.
• Applying advanced tools such as machine learning and artificial intelligence to identify efficiency opportunities. This will not only help bear down on cost at a
time of spiralling energy bills, but it will also support carbon reduction strategies linked to corporate Environmental, Social and Governance programmes, and the achievement of Energy Savings Opportunity Scheme (ESOS) targets.
data and interpreting it in the context of operations and external factors (weather, occupancy levels etc.). This creates ‘fingerprints’ of consumption – and, using AI, the system then progressively learns what best performance looks like. Because the system is smart, it learns to ignore outcomes that are irrelevant, mistaken or due to bad data. Often, it’s a question of spotting improvement
opportunities hiding in plain sight, such as equipment running needlessly or heating controls incorrectly set – and machine learning is the perfect tool to do that by crunching big data. Consumption data generated via AMR systems
can also be fed automatically into monitoring and reporting platforms such as WebAnalyser. This provides an easy way of comparing actual consumption versus benchmark parameters to measure the impact of efficiency strategies. This tool also offers a customisable approach to energy reporting, whether that’s monitoring consumption by period, comparing performance to ‘standard’ operating profiles, validating and analysing usage, or automatically alerting users to unusual consumption patterns.
Energy Assets
www.energyassets.co.uk
CASE STUDY 2: AFE GROUP - HARNESSING THE POWER OF DATA AND PEOPLE
When it comes to energy efficiency, one of the world’s leading manufacturers of professional cooking, bakery and refrigeration equipment has developed a recipe for success. AFE Group, which employs 1500 people over 15 sites, has identified a
sweet spot for optimising energy performance by harnessing gas and electricity data with people power. The result is year-on-year energy performance improvements on the road to Net Zero, supported by metering and data services from Energy Assets. AFE operates multiple
manufacturing, service and contracting centres across Britain – each with distinctive energy profiles. So, the Group provides each site with the autonomy and the tools they need to manage their individual energy footprint. “As a business, we have a clear
aim to lead by example when it comes to optimising energy consumption and operating sustainably,” said AFE Group chief executive officer, Tim Smith. “This means not only monitoring
and measuring the energy we use in granular detail, but also creating a workforce trained and empowered to take responsibility for consumption.”
A 10-YEAR METERING PARTNERSHIP The company receives half-hourly electricity and gas data via Energy Assets’ automated meter reading systems. This data feeds into the WebAnalyser monitoring and reporting
platform, enabling AFE to compare actual performance versus benchmark parameters and to measure the impact of any process improvements. The
tool also enables the company to track the impact on energy costs and carbon emissions of its solar renewables investment. Smith explained: “At one level, the dashboard quickly enables us to
take the ‘pulse’ of energy consumption by day, week, month or over the course of a year and compare it to ‘standard’ operating conditions. At another level, we can dive much deeper into data to validate and analyse usage and spot unusual consumption patterns. “The ultimate aim is to operate as efficiently as possible, to minimise our environmental impact and to make good our pledge to work towards Net Zero.”
LEVERAGING THE POWER OF PEOPLE While data provides the hard operational evidence – and helps drive AFE’s ESOS actions – it is staff engagement that creates the momentum for success. Each business unit nominates an Energy Champion to drive energy and sustainability improvements aligned to targets. This includes a daily energy walk to eradicate waste, identify inefficient equipment usage and flag poor energy habits. In addition, every colleague receives an
AFE Guide to Energy Management. This demonstrates how even small efficiency
gains can enhance the bottom line, support customer sustainability goals and mitigate environmental impact. Regular energy audits also identify areas for improvements, tagged with potential financial and carbon benefits. Smith added: “Our data focus, together with staff training, progress
bulletins and regular audits, gives our people the information and tools they need to support our energy consumption goals. Our aim is to create a culture in which everyone can be an active contributor to our Group-wide Greenlogic energy efficiency, waste reduction and sustainability initiative.”
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ENERGY MANAGEMENT - Autumn 2022
www.energymanagementmag.co.uk
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