Intelligence: more than just data
Energy and water consumption are both measurable and quantifiable yet it very quickly becomes complicated to accurately account for consumption across every part of a complex system like commercial shopping mall or a large scale industrial complex. Keeping track of energy and water consumption within these closed environments is non-trivial and metering systems have to be deigned in such a way that no load or end point is left unmeasured, whether directly accountable financially or not. Every node in the system must have check metering, so called "busbar meters" to ensure that an energy balance at any node can be calculated to ensure that all consumption is accounted for.
Furthermore to gain meaningful intelligence on the performance of a site with regard to the consumption of utilities, the nature of each of the loads must be clearly defined and understood. The nature of the load drawn by a large central chiller will be markedly different from say the main lighting in the common areas.
Traditional water meters essentially provide a running tally of how much water a customer has used. The water bill is based on the current total, minus last month’s total. The utility manager has no idea how much water was actually used on a day-to-day basis, let alone what time of day the most water was used or if water is being consumed after hours.
Most utilities think of smart meters as a way to avoid having to send people around to manually read meters, and the upfront cost of installing them is often too high to justify the money saved on the old-fashioned clip-board method.
MOL stores consumption for both electricity and water at least every 30 minutes in the form of interval data. This is perhaps more typical for electricity metering, but we insist on treating water meters exactly the same way to ensure that we are able to extract the maximum amount of intelligence from the interval data recorded. Simply having start and end readings at the beginning and end of the billing period contributes very little to understanding how and where a utility was consumed.
Only through thorough data analysis and investigation can Intelligence then be derived which in turn informs all further energy management initiatives. Examples are:
- Calculation of baselines against which progress is measured
- Daily/weekly/monthly models that predict consumption of electricity or water under "ideal" conditions that include:
- User definitions of trading types (Preparation, Trading, Merchandising, After hours etc)
- Measurement of actual consumption against models and waste quantification
- Rating against peers
- Tariff and Billing reconciliation (not only forecasting, actual bill reconciliation)
- Monthly reporting on portfolio and site level, including management account reconciliation
