Energy Management: Save The Planet while Saving Money

Energy management help offices be more efficient in terms of energy consumption, and thus, reducing their bills. In average, building offices are responsible of 40% of the consumption of the total energy in the world and everyone knows that the Earth Planet is dying. No Planet B up today. In addition, for companies, the cost of energy is the 2nd most important one for them, just behind the rental fee.

With these two main cost centers, reducing part of them will have a huge impact in the income statement. We wrote about how to reduce the rented sqm in this article. A standard company could reduce the workspace area about 30% before the pandemic and with the new hybrid model, the percentage can raise up to 45-50%, depending on the workforce strategy it will adopt. 

So, energy management, apart from having a clear social impact due to the negative environmental consequences of producing it, has a direct impact on the income statement of the company, as you already know. But what can we do to efficiently manage the energy consumption to reduce it?

SAVING ENERGY COMSUMPTION

Due to the importance of this issue, several things are being done inside the companies:

  • Design, renovation, and management of thermal installations.
  • Efficient indoor and outdoor lighting.
  • Presence control for lighting.
  • Controlling the efficiency of the electric devices.

It is a good beginning and old-fashioned approach. Nevertheless, nowadays, companies can do it much better. The key to energy management is sectorization with occupancy predictions and a dynamic energy management using IoT devices.

WHAT SAVINGS CAN WE ACHIEVE WITH SECTORIZATION ACCORDING TO OCCUPANCY PREDICTIONS

The best way to illustrate what sectorization means is with an example. Let’s imagine an average 5.000sqm office, with the possibility of 5 sectorization areas. In average annual energy consumption per sqm is 210,6 kWh/m² with an average cost of 0.2134€/kwh.

Our example office has an annual energy cost of: 224.300€ + VAT.

Example of Energy Sectorization

With an occupancy analytics system we know which days, hours and how many people are using the space, so we can adapt the use of the energy to the space at the way it is used.

You can control lights with a presence system, but what about climatization. The climatization system, both cooling and heating and broadly, HVAC ones, needs to be prepared in advance to get a good environmental status before employees get in. So, we need a predictive system to know with enough time which areas are going to be occupied, take informed decisions and optimize the energy management while keeping employees’ comfort and machinery’s best practices. People are the one who are important and the main factor influencing over building environmental variables (temperature, humidity, CO2).

From the very beginning, we know that the office has these occupancy figures (see the charts below):

  • Average workday occupancy is around 44%.
  • The most occupied day is Tuesday at 12pm with and average of 64%.
  • Peak occupancy is over 70% in 97% of the time, with no peaks over 80%.
  • Most occupied days are Monday and Tuesday.
  • No significant occupancy variations during the day.
Example Occupancy Figures

The project will be analyzed in three stages:

  1. With the first analysis we realize that 1 zone can be closed during all the period. If we are in a rented space, we could reduce the rented area but if not, we can close this zone and reduce the energy consumption of this area. First savings of 56.000€ per year.
  2. If we go deeper in the figures, the occupancy pattern shows that on Monday and Tuesday we would need 3 zones opened, but on Thursday and Friday we could reduce the available surface to two. So, the reduction of this stage will reach 89.000€ per year.
  3. One year later, after the predictive engine has enough information from the office behavior, the system will have inputs that will allow to the Facility Management Team to plan the HVAC protocols for the spaces. And as much time of data the system analyzes, more precise the predictions will be and higher the savings for the company.

At least, the company in the example could reduce their energy cost by 40% in the first months.

And your company, do you want to improve the World while preventing your company from losing money?