Energy Efficiency Data Analytics a route for improving Plant Performance
Various studies indicate savings in the range of 2 to 4% and up to 20% can be attributed to the diligent application of energy data analytics: these systems are expressly designed to detect and diagnose wasteful use of energy, water and other consumable resources.
What is more beneficial is not only to look at the energy or water improvement but determine what the key metrics to support reliable and efficient operation of plant.
The first step in the implementation of any Energy Data Analytics project involves extensive review of source data and systems. For implementation on existing systems it worthwhile carrying out a desktop exercise to identify not only measurement sources but also data that can be used as proxy data for consumption. System will allow conversion of this data to relatively accurate energy measurement without the need to install additional metering.
Using an Energy Data Analytics system effectively involves relating actual consumption volumes to measurable ‘driving factors’ such as weather conditions and levels of production output. By knowing the driving-factor values, it is possible to estimate the resource quantity required for each ‘stream’ of consumption. Potential inefficiencies or resource wastages are signalled by unexpected discrepancies between actual consumption and anticipated consumption (adjusted to take account of variables such as production output and weather conditions).
It is worthwhile to have validation of data back to source data system
The new Part L guidance for building regulations provides direction on approach to energy monitoring and feed back to building owners.
220.127.116.11 For buildings with an effective rated output for air-conditioning systems of over 70kW a building automation and control system should be installed for the building with the following functions:
(a) continuously monitoring, analysing and allowing for adjusting energy usage;
(b) benchmarking the building’s energy efficiency, detecting losses in efficiency of technical building systems, and informing the person responsible for the facilities or technical building management about opportunities for energy efficiency improvement;
(c) allowing communication with connected technical building systems
and other appliances inside the building, and being interoperable with technical building systems across different types of proprietary technologies, devices and manufacturers, and
(d) monitoring the energy provided by renewable energy technologies.
Also in ISO 50001 (2018) 9.1 Monitoring, measurement, analysis and evaluation of energy performance and the EnMS, with associate standards, gives best practice for Energy Performance Indicators. Using this strategy of significant energy users to develop the approach with Energy Data Analytics solution can truly drive improvements.
The investigation and rectification of any problems highlighted during the Energy Data Analytics process can be integrated into day-to-day operations, with overall management responsibility allocated to the relevant engineering teams.
With reporting tools systems can be designed to track the daily/weekly kWh consumption of (as well as running costs of) each asset e.g. chillers, boilers, compressor plant, office air-handling units. Worthwhile to tracks weekly water consumption/discharge. Deciding on a frequency of reporting that work best for the orgranisation and the risk of deviance is recommended.
Among a range of benefits, it can assist management team with the process of establishing aggressive, but realistic, energy-reduction targets. Detect any persistent, unexplained consumption patterns; facilitates the diagnosis of excess costs, the causes of which may not become evident during physical inspections. It enables the process of reporting and analysis to be conducted in such a way that the variable effects of all major driving factors are properly accounted for. Further, it quantifies the savings achieved by energy management activities, and verifies the savings delivered by individual projects, again taking variable influences into account so that factors such as changes in production output and changes in weather conditions do not distort report findings, and thus cannot be used as a way of concealing poor performance.