Collecting and analysing additional data to provide an enhanced picture of the performance of an asset is the subject of this blog post.
First let’s consider the issue of building energy consumption and specifically, natural gas use for space heating. To determine whether more or less gas was consumed than the previous period and/or whether energy efficiency projects were effective, it is necessary to monitor more than just the amount of gas that was consumed.
Natural gas consumption for space heating use varies with ambient temperature. More gas is usually consumed in winter months than in summer and more gas is burned in very cold winters than in warmer winters. So to establish a more precise picture of actual building performance it is necessary to also capture ambient temperature data. This is usually done by collecting degree day data which provides a discrete measure of how cold or warm it was in a particular location on a particular day, (week or month). With this additional data it is then possible to analyse specific energy consumption in kWh per degree day. Hence if I know how many degree days in a month I can predict the expected energy consumption and compare it with the actual to determine performance.
A similar method can be applied in an industrial context to determine specific energy consumption where energy consumption varies according to production activity e.g. widgets made, man-hours worked, etc.
Another example of where additional data capture can be used to provide a more precise digital twin, is that of vibration monitoring to determine the condition of rotating machinery. Vibration sensors (accelerometers) can be easily retrofitted to appropriate points on rotating equipment e.g. motors, compressors, gear boxes, etc. and used to detect the deterioration of components with the aim of preventing catastrophic failure.
More data, can provide an enhanced picture of past performance and used to predict future performance but the next challenge (and the harder piece) is turning this intelligence into action that results in improved performance. We’ve seen lots of examples of very clever (and expensive) data collection and reporting systems that are ignored because the intended users don’t understand what the system is telling them and how they need to respond.
Assistance – the topic of the next blog post.