A fundamental part of effective energy management is the routine comparison of actual consumptions with expected values, and those expected consumptions need to be calculated as accurately as possible. Underlying this is the idea that consumptions vary in line with the weather, production activity levels, available daylight, mileages or other such measureable factors, with formulae being established that relate each consumption to the relevant factor or factors.

In some literature you will see these factors referred to as ‘independent variables’ or ‘relevant variables’. They do indeed need to be relevant and independent measurements, but the term ‘driving factor’ better describes what they do: they are the things whose day-to-day variation drives variation in energy consumption.

As well as causing variation in consumption and itself routinely varying through time, a driving factor needs to be expressible as a numerical value. For energy-intensive products this is often as simple as counting the throughput quantity; where applicable, weekly hours of darkness and vehicle mileages are equally straightforward. Dealing with the weather is more complex. The most significant variable here is outside air temperature. However, the response of a heating system to variation in temperature is not linear because there will always be an outside temperature above which demand is zero. The universal solution to this problem is to track the outside air temperature more or less continuously and convert the data into a measure called the degree day value which can be reported, as a single number, at weekly or monthly intervals (typically). A building’s heating fuel consumption can be expected to correlate with degree-day values for the region in question.

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