ENERGY MANAGEMENT & OPTIMISATION
Data – the currency unlocking energy efficiency
There’s probably never been a more important time for industrial and commercial (I&C) organisations to get ‘energy fit’ as part of their wider sustainability goals, says David Sing, managing director, Metering & Data at Energy Assets
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piralling gas and electricity costs are placing real pressure on operating budgets so, self- evidently, any reduction in consumption will be much prized. Also, from April, the
government’s revised Energy Bills Discount Scheme will scale back support for most non-domestic users. The good news, though, is that energy managers now have more advanced tools at their disposal than ever before to help root out waste, particularly in the analysis and interpretation of consumption data. Monitoring and reporting platforms that assimilate half hourly data from automated meter reading systems are relatively commonplace. These provide a convenient way of comparing actual consumption against benchmarks, enabling managers to measure the impact of efficiency programmes. These tools, such as WebAnalyser, support a customisable approach to energy reporting, whether monitoring consumption by period, comparing performance to ‘standard’ operating profiles, validating and analysing usage, or automatically alerting users to unusual consumption patterns. Moreover, the value of data will only grow with the onset of the Market-wide Half-Hourly settlement (MWHH) reform due by 2025, which for I&C users will likely create opportunities for demand side response incentives and preferential time of use tariffs. That said, turning meter readings into efficiency strategies pre-supposes that the underpinning energy performance profiles for each building are accurate enough to be used as benchmarks.
This is why machine learning, informed by artificial intelligence (AI) is fast emerging as a favoured route to efficiency. Machine learning systems, such as our AMR DNA platform, can crunch years’ worth of metered energy data in short order and progressively ‘learn’ what optimal performance looks like for each building.
This creates pattern recognition that can spot tell-tale signs of energy waste unique to each building, such as equipment running needlessly, or heating controls incorrectly set. As the system continuously monitors consumption data, it identifies potential areas of waste energy needing attention until benchmark performance is achieved. As such, machine learning analytics can be a complementary tool to monitoring and reporting platforms, ensuring available systems are working in harmony.
The science of energy waste
Pinning down portfolio-wide energy waste and improvement opportunities requires rock-solid benchmarks to compare like-with-like. Firstly though, it’s worth focusing attention on what we mean by ‘waste’ because it comes in many forms:
• Precedent waste - where a building does not perform as well as it has in the past (and noting that operational contexts and use-cases of a building will change and must be re-learned).
• Routine waste - where a building can be shown to systematically use energy that cannot be necessary or comfortable (e.g., if heating is maximised at +5°C, since colder weather requires more heating; a combination of discomfort or waste exists at all temperatures between -5°C and +5°C).
• Peer or benchmarked waste - where a building does not comply with its peers (for example, sets of comparable buildings are expected to have similar balance temperatures, night-setback loads and apparent occupancy patterns).
The analytics engine inherent in AMR DNA, 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 opportunity. However, it is also important to acknowledge (and quantify) the impact of efforts made. Accordingly, as illustrated below - proof- of-savings reports can include graphic
26 BUILDING SERVICES & ENVIRONMENTAL ENGINEER APRIL 2023
comparisons of prior performance and any change at the click of a button.
Energy Consumption Comparison Report This report compares a period of 796 days from 2020-01-17 to 2022-03-22 with a period of 298 days after the implementation of machine learning changes from 2022-03-23 to 2023-01- 14. Each period has models of consumption in response to outside air temperature for each day of the week. These are then applied to a typical annual weather pattern for the location to come up with expectations of annual performance on the new and old basis. The difference represents a saving which might be expected if the performances prior to and after changes were replicated for the hypothetical typical year: Before kWh = 1,522,658; After kWh = 1,044,713; Saving Energy 477,945 kWh; Saving Percentage 31.4 %. The bottom line represents the cumulative deviation from expected consumption, where ‘Up’ is wasteful and ‘Down’ is ahead of expected. The turquoise section represents the period benefiting from machine learning.
Reliability Observations Report
Valid Proof of Savings requires sufficient time period to be considered, during which a representative spread of weather conditions are represented for each day of the week. The blue represents ‘before’ machine learning influence, the turquoise line is ‘after’. Energy managers will, of course, use their
professional expertise and knowledge of each building to apply machine learning data reports to those areas where action can positively impact on efficiency.
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. Fortunately, machine learning can help unpick waste by thorough historical analysis of half-hourly meter data and interpreting it in the context of how and when the build is operational, taking account of external factors (weather, occupancy levels). When primed with this information, these tools can assist managers in optimising energy performance across their entire building portfolio.
Read the latest at:
www.bsee.co.uk
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