IC-JANFEB23-PG24+25_Layout 1 31/01/2023 11:32 Page 24
BUILDINGS & FACILITIES MANAGEMENT
OPTIMISING ENERGY EFFICIENCY THROUGH DATA
Spiralling energy costs mean that any improvements they generate will likely deliver a quicker return on investment than might previously have been the case – and, in the bigger scheme of things, they will also be contributing to decarbonising the wider economy. The good news for facilities managers is they now have more data-driven tools at their disposal than ever before both to influence energy efficiency and to report on improvements. Moreover, the value of this increasingly data- focused environment will only increase with the onset of the Market-wide Half-Hourly settlement (MWHH) reform due by 2025, which for I&C users will likely create demand side response incentives and preferential time of use tariffs. So, today, the most pressing question for facilities managers is where best to invest time and resources to improve efficiency, bear down on consumption costs, reduce carbon emissions and meet their Energy Savings Opportunity Scheme (ESOS) obligations? At one level, this means optimising the value of energy metering, consumption monitoring and data analytics; at another it means evaluating advanced technologies, such as machine learning and artificial intelligence to drive out waste. Getting all these systems working in harmony is critical to optimising energy efficiency and reducing carbon emissions.
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As a start point, facilities managers need to answer the following questions:
Are you capturing consumption data in granular detail using automated meter reading (AMR) systems?
Are you monitoring and analysing this data through advanced AM&T portals, such as WebAnalyser, and setting automated alerts for unusual consumption patterns?
Have you evaluated the potential benefits of using advanced tools such as machine learning and artificial intelligence to root out unseen efficiency opportunities?
here has probably never been a more critical time for facilities managers in industrial and commercial (I&C) settings to take a hard look at their energy efficiency.
By David Sing, managing director, Metering & Data at Energy Assets
HEAVY DATA LIFTING Machine learning, such as that employed by AMR DNA from Energy Assets, uses artificial intelligence (AI) to progressively improve energy performance.
It does this by assimilating half-hourly meter data and interpreting it in the context of operations and external factors (weather, occupancy levels). This creates ‘fingerprints’ of consumption – and, using AI, the system then progressively learns what best performance looks like.
Often, it is 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 big data analytics tool to do that. For complete building energy performance oversight, meter data generated via AMR systems can be fed into monitoring and reporting platforms such as WebAnalyser. This provides an easy way of comparing actual consumption versus benchmark parameters and to measure the impact of any efficiency strategies. This tool also offers a customisable approach to energy reporting, whether that is monitoring consumption by period, comparing performance to ‘standard’ operating profiles, validating and analysing usage, or automatically alerting users to unusual consumption patterns.
24 JANUARY/FEBRUARY 2023 | INDUSTRIAL COMPLIANCE
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