Category Archives: Data collection

Audits and surveys, Data collection, Energy analysis and reporting, Transport

Tracking performance of light vehicles

Here is a monitoring challenge: suppose you want to do a weekly check on the performance of a small fleet of hotel minibuses. Although you can record the mileage at the end of each week, you will have a lot of error in your fuel measurement because you’ll only know how much fuel was purchased but not when. How can you adjust for the inconsistent fuel tank level at the end of the week?

One method would be to use the trip computer display which will show the estimated remaining miles (see picture). The vehicle in question has a 45-litre tank: at its typical achieved average mpg, it has a range of 613 miles of which it has used 39%, so we can add 45 x 0.39 = 18 litres to our calculated fuel consumption. Note that we will need to deduct an equal amount from next week’s consumption, and this “carry forward” is likely to reduce the error in the adjustment.

This procedure also helps if drivers do not consistently fill to the top. To the extent that they underfill on the last occasion in the week, the shortfall will increase the adjustment volume to compensate. The adjustment can only ever be approximate, however, so it’s better if they consistently brim the tank.

The other advice I would give is to track not miles per gallon (or any similar performance ratio) but to plot a regression line of fuel versus distance. This will pick up, and detect changes in, idling behaviour.

Data collection, Project profiles

Automatic metering disaster recovery

Our client relies on an extensive network of automatically-read submeters thoughout his estate and asked us to prepare a recovery manual in case his data-collection contractor should cease trading. As part of the exercise we set up a temporary online storage location, proved that the output from a typical data-logging installation can be rerouted, and established what format the data arrive in.

We are also discussing with the incumbent contractor what  additional information will need to be available in escrow to permit an orderly handover.

Data collection, Energy analysis and reporting

Justifying additional meters

Additional metering may be required for all sorts of reasons. There are three relatively clear-cut cases where the decision will be dictated by policy:

  • Departmental accountability or tenant billing: it is often held that making managers accountable for the energy consumed in their departments encourages economy. Where this philosophy prevails, departmental sub-metering must be provided unless estimates (which somewhat defeat the purpose) are acceptable. Similar considerations would apply to tenant billing (I am talking about commercial rather than domestic tenants here).
  • Environmental reporting: accurate metering is essential if, for example, consumption data is used in an emissions trading scheme: an assessor could refuse certification if measurements are held to be insufficiently accurate.
  • Budgeting and product costing: this use of meter data is important in industries where energy is a significant component of product manufacturing cost, and where different products (or different grades of the same product) are believed to have different energy intensities.

The fourth case is where metering is contemplated purely for detecting and diagnosing excessive consumption in the context of a targeting and monitoring scheme. This may well be classified as discretionary investment and will require justification. This could be based on a rule of thumb, or on the advice in the Building Regulations (for example). A more objective method is to identify candidates for submetering on the basis of the risk of undetected loss (RoUL). The RoUL method attempts to quantify the absolute amount of energy that is likely to be lost through inability to detect adverse changes in consumption characteristics. It comprises four steps for each candidate branch:

  1. Estimate the annual cost of the supply to the branch in question (see below).
  2. Decide on the level of risk (see table below) and pick the corresponding factor.
  3. Multiply the cost in step 1 by the factor in step 2, to get an estimate of the annual average loss.
  4. Use the result from step 3 to set a budget limit for installing, reading and maintaining the proposed meter.
Risk Typical characteristics Suggested
factor*
High Usually associated with highly-intermittent or very variable loads under manual control, or under automatic control at unattended installations (the risk is that equipment is left to run continually when it should only run occasionally, or is allowed to operate ‘flat out’ when its output ought to modulate in response to changes in demand). Examples of highly-intermittent loads include wash-down systems, transfer pumps, frost protection schemes, and in general any equipment which spends significant time on standby. Typical continuous but highly-variable loads would include space heating and cooling systems. It should be borne in mind that oversized plant, or any equipment which necessarily runs at low load factor, is at increased risk. 20%
Medium Typified by variable loads and intermittently-used equipment operating at high load factor under automatic control, in manned situations where failure of the automatic controls would probably become apparent quickly. 5%
Low Anything which necessarily runs at high load factor (and therefore has little capacity for excessive operation) or where loss or leakage, if able to occur at all, would be immediately detected and rectified. 1%

*Note: the risk percentages are suggested only; the reader should use his or her judgment in setting percentages appropriate to individual circumstances

The RoUL method tries to quantify the cost of not having a meter, but this relies on knowing the consumption in the as-yet-unmetered circuit. The circular argument has to be broken by estimating consumption:

  • by step testing
  • using regression analysis to determine sensitivity to driving factors such as product throughput and prevailing weather
  • using ammeter readings for electricity, condensate flow for steam, etc.
  • multiplying installed capacity by assumed (or measured) load factors
  • from temporary metering