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6 Ways Big Data Can Address Energy Consumption Analytics

Image of Natalie Patton
Natalie Patton

With climate change accelerating and built environments accounting for nearly 40% of energy use worldwide, energy efficiency has become a key objective for building management. Smart building technology offers tools for achieving optimal energy efficiency using IoT sensors that generate comprehensive data on equipment functionality and environmental conditions. With the assistance of automated building management systems (BMS), analytics software, and big data for building energy management, energy consumption is more easily controlled and occupant comfort is improved.

1. Understand Your Consumption

No simple metric exists to determine exactly where and how buildings use energy, but a good starting point entails examining the kilowatt-hours (kWh) a building uses per square foot. 

In a typical commercial building, energy consumption is broken down as follows:

  • Cooling equipment like fans and air conditioners: 3 kWh/square foot.
  • Heating equipment: 2 kWh/square foot.
  • Hot water heating: 0.5 kWh/square foot.
  • Lighting: 7 kWh/square foot.
  • Refrigeration equipment: 8 kWh/square foot.
  • Ventilation: 2 kWh/square foot.

This average of 22.5 kWh per square foot gives facilities managers a base from which to evaluate energy use.  

Tracking that energy use, however, requires more than a look at your utility bills, and most buildings don’t have the metering to do a detailed breakdown of energy use. Understanding how much energy different spaces and systems consume—as well as when and why it is being consumed—requires an advanced analytics platform that can gather information from all connected systems and transform big data into actionable insights. 

2. Optimize Continuously

One of the greatest benefits of adding intelligent analytics to a BMS is the potential to continuously optimize how the system uses energy, altering usage as conditions change. Automated systems with machine learning capabilities can adjust and refine a Building Energy Management strategy by understanding the patterns that help buildings achieve optimal efficiency. To do this, smart sensors gather data from different parts of a building to determine which areas use the most energy. The more sensors a system has, the more data the analytics platform has to evaluate, allowing for faster identification of waste and resolution of inefficiencies. 

This is true even in buildings equipped with energy-saving technologies. For example, installing LED lighting can make a big difference in energy consumption, but cutting-edge smart technologies have the potential to create even greater savings. A truly smart lighting system consists of:

  • LED lighting to reduce energy usage
  • Sensors to gather data
  • Control units that adjust lighting as needed
  • Connected networks that allow communication between devices and systems
  • Analytics software to evaluate gathered data and determine the best ways to achieve energy efficiency 

With these components, you gain new and powerful ways to modulate energy consumption on an ongoing basis rather than relying on static systems.

3. Identify Times of Peak Demand

With analysis of big data, determining peak demand times isn’t a matter of guessing. Analytics platforms with machine learning capabilities can use big data to create highly accurate forecasting models and adjust energy consumption accordingly, or help you mitigate higher usage by taking concrete steps to reduce the load. 

For example, sensors on a building’s south side indicate that this portion of the building uses considerably more electricity during the summer because more cooling is required to keep this area comfortable. Cooling demand increases as the sun shines through the building’s large, south-facing windows in the afternoon. When sensors have identified the problem, you can find solutions, such as using automated blinds that keep these windows shaded in warm summer months, minimizing reliance on energy-intensive air conditioning. 

Accurate forecasting also allows you to rapidly identify unexpected changes. Both greater and lower-than-predicted energy use could point to compromised equipment performance, unusual occupant behavior, or problems with the building itself, such as compromised insulation. 

4. Correct for Real-World Conditions in Real Time

IoT sensors are central to any smart system, but their true value can only be realized when they are sensing the right things. Supported by big data from key environmental sensors, energy consumption can be fine-tuned to adapt to changing conditions both within and outside the building. 


Weather conditions invariably affect indoor environments; at its most simple, higher summer temperatures cause air conditioning units to run longer, while cold winters require more energy for heating. But simply turning the AC on when summer hits or setting the thermostat to 68° F in the winter can result in significant waste, as outdoor conditions and occupant needs can fluctuate day to day and even hour to hour. By gathering data on outdoor weather conditions, smart systems can regulate energy use better, in real-time, and even in advance of predicted changes.


While sensors can easily recognize variables like weather conditions, determining building occupancy offers one of the more difficult challenges. Estimating occupancy often relies on historically-based schedules. For example, shopping malls tend to be particularly busy on Saturdays, while schools will use less energy when students are on summer vacation. 

Yet such baselines are not without flaws. Take the 2020 COVID pandemic, which threw off traditional methods for predicting occupancy. Office buildings traditionally open Mondays through Fridays were empty, as were many retail businesses that normally experienced high weekend traffic and restaurants that usually filled up in the evenings. 

In the near future, micro-location GPS applications may very well be able to determine occupancy in real time with considerable accuracy, even within a few inches. Though IoT devices have not quite reached this point, as current GPS technology cannot yet locate entities with such accuracy, the sophistication of such equipment is moving towards this and several technologies already exist to track real-time occupancy. These include:

  • Bluetooth low energy (BLE) beacons
  • Ultra-wideband (UWB) micro-location 
  • Wireless positioning systems
  • Magnetic field mapping
  • Radio-frequency identification (RFID)

Big data drawn from such sensors can be used to adjust energy-consuming equipment throughout entire buildings or in individual rooms, including lighting, heating, cooling, and ventilation. Not only does this reduce waste, it also makes the building responsive to occupant needs.

5. Identify Equipment Problems

Even with the availability of big data, energy consumption may still remain high—and the cause may not be readily evident. For example, if the HVAC system is simultaneously cooling and heating the building, or a fan runs constantly within a portion of the ventilation system, energy usage can increase significantly. While energy consumption data can show this increased usage, identifying exactly where inefficiencies occur may take some detective work. 

IoT and analytics help detect equipment defects, alerting staff to anomalies in performance and energy consumption that should be investigated. Finding such irregularities identifies areas where efficiency can be improved through maintenance, repair, or replacement of equipment. The most advanced analytics platforms can even suggest solutions for better short and long-term energy management and allow for both remote and automated adjustments.

6. Making Sense of Big Data and Reducing Energy Consumption

Smart technologies are giving us powerful new tools to understand and manage energy consumption in commercial buildings. While future-focused buildings must rely on complex networks of equipment, sensors, and devices, analytics lie at the heart of any smart building system. Choosing an intelligent analytics platform, like onPoint, allows you to get the most out of big data and reduce energy consumption in meaningful ways, helping you see a real return on your investment.

To see onPoint in action watch a 5-minute demo video here.

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