Energy data current situation
The EIA expects global energy demand to increase by 47% over the next 30 years, driven by population and economic growth.
Buildings are key to a low-carbon future. In fact, according to research by the World Economic Forum, in Europe, more than 220 million buildings are energy-inefficient, many relying on fossil fuels.
Tracking energy usage in these buildings is crucial.
If you’ve benchmarked your buildings’ energy performance, you know its value. This is vital for any energy efficiency plan.
In fact, energy benchmarking, as an ongoing process of reviewing the energy consumption of a portfolio, allows you to identify improvements in overall energy performance, by identifying underperforming sites and good opportunities for savings.
For more information about the energy benchmarking
Further reading
Portfolio ranking
By the end, you should have a ranking of your sites from best to worst based on kWh consumption.
But how reliable is your ranking? Simple benchmarking may miss hidden valuable information.
This is where analyzing opening and closing hours helps, making your data meaningful.
In fact, it is all about breaking down your building’s operating time into meaningful periods and extracting the information you need to drive an energy efficiency plan based on even more accurate insights into your energy data.
If you haven’t fully explored benchmarking, read on to learn why this deep dive is crucial.
Table of Contents
What do we mean by opening and closing hours?
When we talk about opening and closing hours of a building, we usually refer to 3 different concepts:
Opening hours based on customer activity
In this scenario, we refer to the typical opening and closing hours that a store/shop may have: 9 am to 6 pm on weekdays and 10 am to 7 pm on weekends (Saturday only), for example.
(This also means that your analysis should take into account national days and other holidays when the building is closed).
Opening and closing hours based on staff working hours
In this second case, we are talking about the working and non-working hours of the shop’s employees, who must certainly enter the premises before the customers and leave after the last customer has left.
During these times, the staff must carry out certain activities, such as cleaning, switching the lights on and off, turning on/off larger equipment… All of this involves energy consumption that is linked to a period of time that does not coincide with the opening hours for customers.
Individually defined periods
It is also possible to look at defined periods within the day in order to analyse more precisely the relevant behaviours we want to identify.
In general, the most common user-defined periods are off-peak and peak periods. Off-peak periods usually occur during the hours of lowest human activity, such as the night hours. The following examples explore situations of off-peak consumption during the night.
Off-peak: Night time
The night hours between 2 am and 5 am are the quietest time of the day in an office or shop, as no one is working and there are no customers (apart from exceptions, which we will analyse later).
We can use this knowledge to eliminate the influence of human behaviour on consumption by analysing only these hours of the day (e.g. as an average per hour).
This leaves us with pure background consumption which can be more accurately attributed to one or more sources of consumption.
For example, we may have idle consumption from fridges, air conditioning, or other standby equipment, as well as a dripping tap causing a small leak in the network. The sources can be many, but by defining thresholds for ‘normal’ consumption behaviour during these off-peak hours, we can detect anomalies, or in other words, potential problems.
This is the game changer: without a specific breakdown and subsequent analysis of peak and off-peak periods, the total, or average consumption of an entire day will hide all these potential issues that would simply be impossible to detect.
With energy management software like Energis.Cloud, which sends you real-time notifications when such anomalies occur, you can intervene quickly, resulting in financial and time savings.
Peak period
Depending on the specific situation, a facility may have a peak in the afternoon hours (e.g. supermarkets) or in the evening hours (e.g. restaurants open for dinner only).
In this case, we can use these peak periods to provide an accurate benchmarking analysis, adapted to the hours of use of the building.
In general, peak times need to be defined more individually than off-peak times, as most buildings/stores have their off-peak times during the night hours, while peak times can vary as described above.
What is the added value of having this additional information?
As we have seen, staff must complete tasks before customers enter a retail store or shop.
This can take hours, varying with building size and purpose (e.g., restaurant, café, shoe shop).
Consider two identical restaurants in the same chain with the same hours.
They open at 11 a.m. for customers and 9 a.m. for staff. They close at 10 p.m. for customers and 12 a.m. for staff.
Which is less energy efficient?
As you will recall, for benchmarking purposes it is necessary to either use a criterion to calculate energy consumption or to normalise the calculation, and the best way to do this is to use the average consumption of all sites.
But just looking at the average consumption over the day may not be enough.
In this example, it’s better to use the opening and closing hours based on staff working hours to detect abnormal consumption and/or different behaviour between the two.
Perhaps this analysis will reveal that in the second building, overnight consumption is higher than expected because staff tends to forget to turn off the air conditioning, which runs all night even when it is not needed.
By simply taking the average consumption of the two, this inefficient behaviour can be very difficult to detect.
The analysis is so powerful, yet flexible, that you can focus on time windows that reflect the true energy behaviour of the building.
The important information is still there, and if you know how to access and use it, you can stay one step ahead of your competitors.
What is needed to calculate this consumption?
There are four steps you need to take to effectively calculate this consumption:
- Collect your data.
- Calculate the consumption.
- Visualise the results.
- Act and follow up.
Collect your data
Consumption data is the fuel of benchmarking; nothing can be done without consumption data for all sites.
To collect this data, it is very helpful to use the main meters. They are located where the source of the primary (most important) energy you want to monitor is, so you can get the main consumption.
This data is usually available thanks to the Distribution System Operator: it is not only responsible for the distribution of energy, but also for the collection and storage of consumption data.
In fact, the data is entered daily, every 10 or 15 minutes for electricity and every hour for gas.
For more information about retrieving data with DSO
Further reading
For example, with Energis.Cloud, once you have connected all the main meters you need, you will see all your consumption data uploaded to the platform, with the detailed value for each site.
Secondly, you need to have the time information (hours and minutes), either for all sites or for a specific site.
Calculate the consumption
Two types of data are needed to calculate this consumption:
- Consumption data (live data at regular intervals, at least hourly).
- Information on the start and end time of the desired period.
With these two pieces of information, the consumption during (or outside) the selected period (peak, off-peak, individual) can be automatically retrieved.
This method is really powerful because you can define and modify the level of complexity according to the profile you want your analysis to have.
Visualise the results
Once the energy management software ingests the data, it displays it graphically, e.g. with a ranking chart showing the performance of different sites.
Since interpretation is an activity that needs to be done on a regular basis, it is easier to use an EMS that presents the data with a good graphical interpretation so that you don’t waste time scrolling unnecessarily.
For this reason, the Energis.Cloud graphs show the sites ranked according to their consumption expressed in kWh, from the lowest (‘the best) to the highest (‘the worst’).
In this way, you can see which sites require intervention and extend this analysis to the full portfolio.
Act and follow-up
The energy benchmarking results are useful for assessing the current situation, and as we saw going deeper is the key to having a realistic and detailed bulletproof analysis. Also, you know that without an action plan, there will be no improvement.
This is where you put into practice what you have learned from the analysis.
Once the action plan is in place, you should track and monitor progress. A good way to follow up is to automatically receive reports on a weekly or monthly basis so that you are always in control of the situation.
The added value that Energis.Cloud brings
With intuitive, talking charts, drag-and-drop functionality, and the ability to collect data from DSOs in different countries, as well as weather, occupancy, and activity data, Energis.Cloud enables you to make the best energy-saving decisions ever.
If you’re looking for simplicity, and especially if benchmarking energy consumption is new to you, contact one of our experts.
We will be glad to help you scale towards sustainability and energy data management.
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