Lighting, power for office equipment, heating and cooling systems, and water consumption are some of the many services that companies need to stay in business. Office buildings can potentially consume huge amounts of energy while supporting business activities and whether a company owns or leases a building, it’s important for them to think about how to maximize energy efficiency. Any potential energy savings translate directly to the business bottom line.

To turn these potential savings from theoretical improvements into actual savings, companies need to understand where they are using energy and why. Data collection systems within a building can be tracked for energy usage snapshots for daily business activities and to identify opportunities to minimize energy consumption. The information from data collection systems can be passed over to the facilities management team as part of managing the use of energy within a company. While these different energy bills are often not collectively analyzed in detail, the opportunities for energy savings are often sporadic rather than strategically planned for.

The Internet of Things, sensor data and linking data sources together

Industry analyst firm IDC predicts that spend on Internet of Things (IoT) devices and solutions is expected to go up to $1.7 trillion worldwide by 2020, with around 32 percent of this total spent on sensors and modules. While these new data sources provide companies with more information on how energy is being used and distributed in their buildings, the challenge is linking these data sources together to create value. There are two primary aspects to a IoT data platform: one, it can collect and manage large volumes of time series data and two, it can analyze data to make it useful for companies to do smart things things with it. These are the two sides of the same coin.

As each sensor creates data, companies need a platform to save this data while keeping up with the amount of ongoing data being created. Every sensor update or change is important - missing an individual change means the data is less accurate and less likely to provide potential valuable insight to the business. With traditional database management systems, it can be hard to cope with the sheer amount of data that systems within buildings can create. The rate of data creation can be high for a single building system with tens or hundreds of sensors. Now, imagine multiple systems with thousands of sensors across various locations. You have to be able to handle the veritable deluge of data every day.

It’s also important to look the data from a set of sensors over time. This type of data set - time-series data - can be analyzed to identify patterns and potential room for improvements. By looking at various sets of time-series data, companies can identify opportunities for tangible improvements to be made. During the analysis phase, keep in mind that making changes to one system can have unintended consequences for another. For example, let’s imagine a company trying to reduce its HVAC bill by cutting the amount of cooling by a couple of degrees. This might reduce the cost of overall cooling, but it may lead to further spend on more specific cooling for the IT team, or more costs associated with refrigeration. While individual budget holders might be pleased, the overall cost to the business may end up the same.

Improving energy utilization and efficiency with IoT data

For companies with multiple locations, energy usage patterns will differ at each site - for example, some may have different requirements based on size or externals conditions. IoT data can help companies make decisions around energy that are more informed and deliver better results. Based on sensor data, companies can better understand how individual locations are performing and drill down into specific networks or systems within these locations. By linking up multiple sources of time-series data and analyzing energy usage in context, companies can balance what has to be consumed and where efficiencies can be made.

This analysis involves looking for “like for like” situations, where locations can be grouped into cohorts. For example, if a retailer has multiple stores that are different sizes, it makes sense to analyze those stores based on size criteria. Energy use within the large stores will be very different to smaller ones or those within shopping malls, for example. It’s also worth looking at similar external factors - buildings in colder climates will have different energy use profiles compared to those in warmer climates.

By analyzing these cohorts, companies can potentially see the opportunities to reduce energy use across the group, leading to savings. It’s also possible to look within that cohort to see outliers where energy spend is much higher than the average. This can then lead to further improvements through specific actions at that location, whether this is based on store design or amending staff processes to reduce energy waste.

The important element here is that each system within each and every building will create time-series data that can be used to analyze operational performance. This data is constantly being created and all of it has potential valuable, so it all has to be captured. Looking ahead, companies will rely on this IoT data to make decisions across their activities. It’s only by putting this operational data in context that building facilities can be managed to save energy and ultimately money.