Are you wondering why your energy bills show that your recent energy efficient retrofits haven’t created the kind of energy savings you had forecasted? Changes in weather, operations, or sales volume may be obscuring your energy savings. To truly understand how each site is performing, you need to normalize your energy bill data, removing major drivers of fluctuations in your facilities’ energy consumption. Normalize your energy bills to compare how much energy you would have used with actual consumption to identify circumstances for abnormal energy use. The key ingredient to normalizing your energy bills is the creation of a benchmarking process.
How to Normalize Your Energy Bills
1. Compile Consumption & Expense Data
One of the most critical items of energy benchmarking is the data. The first step is to identify the appropriate data sources. It is important to get consumption and expense information from reliable and consistent sources. This information can be acquired at the site or regional level on a monthly or quarterly basis, and smart meters are becoming an efficient way to gain access to the data in real-time.
2. Identify Relevant Facility Consumption Data
The next step to building a benchmark is learning what facility information you can get access to that is accurate and up to date. Questions you will want to answer include:
- Do I have corporate information from my facility (hours of operations, number of employees, equipment)?
- Do I understand the characteristics of my facility (stand-alone building, multi-story, drive-through, hours of operations)?
When looking at your benchmark data, be sure to use per square-foot basis metric to help you group your company’s facilities into performance tiers.
3. Understand the Operations of the Facilities You Are Benchmarking
The last piece that drives consumption and changes in consumption is operations. Facility operations can vary by location and use. As an example, some locations could be open during the week while others stay open only on weekends. Within operations, you also need to account for the various equipment that is in use at each site; e.g. pizza ovens, washing machines, or walk-in freezers. There could also be variances in occupancy, production units or sales volumes that could potentially drive consumption of a specific building.
Energy Bill Normalization
Once you gather all of your critical information, your data, facility characteristics and other operating information, the next step is to understand what you need to normalize results for. There are many common variations in how utility companies bill their customers. For example, energy bills can be invoiced for up to 34 days. A common normalizing technique to handle this irregularity is to conduct calendar normalization. So for benchmarking for a specific month that has 30 days, you will want to make sure each building has been analyzed only for the 30 days of the targeted month.
Another common exercise is weather normalization. If your facilities are located all over the country or the world, then you will want to be able to benchmark them accurately by removing the impact weather had on each of your facilities. Typically weather is normalized in per square footage or production units. Other examples of normalizing your energy bills include analyzing operating hours or sales transactions.
While normalizing energy bills is usually conducted for financial reasons, you can also experience operational efficiencies. One of the most common excuses site managers make for higher energy is either their geographic location or foot traffic. Once you normalize the ways those activities are conducted, you can then hold people accountable for what’s going on in their facility and you get more accurate comparisons between the sites. This step makes stakeholders more aware of their performance and drives everyone to become more responsible about their energy use.
If you have decided that you want to normalize your energy data, make sure you have access to the appropriate data. You will end up spending some time looking at variables and the correlation between changes in occupancy, sales transactions, equipment purchases, and other variables that impact your energy use and operations.