The energy efficiency of buildings, whether commercial or industrial, continues to be a significant issue since they are among the largest energy consumers. The most effective approach to improving energy efficiency in buildings is by using energy-forecasted data to optimize decision makings. For the last decade, artificial intelligence (AI) and machine learning (ML) have been extensively utilized to analyze and forecast building energy consumption and performance.

Most systems in buildings today incorporate smart devices such as sensors that gather information. However, the question remains, what can you do with the data collected by these sensors? With machine learning, this data can be analyzed to effectively uncover patterns, predict preferences, and automate responses.

Department of Energy

According to the U.S. Department of Energy, as much as 30% of building energy consumption can be saved through the effective use of controls enhanced by using machine learning.

For example, let’s look at lighting networks in commercial and industrial buildings. With machine learning, these networks have the ability to understand the environment they operate in, and the occupants’ lighting preferences better and more effectively than without machine learning.

Lighting networks with machine learning take advantage of all the input data and learn from it how to automatically optimize the operation of every single lighting fixture—minimizing human interaction. Considering the hundreds, if not thousands, of lighting fixtures in a single building, the energy savings could be enormous.

Data collection and analysis are indeed a crucial part of the equation in PoE lighting technologies.

Machine learning (ML) algorithms continuously incorporate and analyze the data collected from various sensors. The algorithms can then formulate an internal model that is mine for patterns and used to identify deviations. Over time, the model develops a deeper understanding of the building environment and performs more complex learning processes to independently adjust operational preferences, take actions, and make changes to networks and device behaviors. All of this assists in identifying savings opportunities and recommending solutions for various issues.

How Can Machine Learning Improve Energy Efficiency?

Forecasting Consumption

One of the major roles of machine learning is the prediction of energy consumption. ML analytics can utilize gathered energy consumption details to determine trends and foresee future energy use. In instances when actual energy consumption is greater than expected, the system could immediately alert building owners to an inefficient practice.

Modeling

Machine learning can create models of your building based on data collected from sensors. And takes into consideration the surrounding environment and occupancy levels. With these intelligent models, automatic actions can perform to dynamically adjust lighting levels conforming to occupant needs. While also enhancing utilization and lowering energy consumption.

Expecting Faults

A building’s interconnected network of devices can generate a substantial—and often impractical—number of alerts in case of malfunctions. ML techniques are capable of analyzing. And prioritizing the alert data to develop insights that are more meaningful and avoid false alarms. Moreover, the system can alert you of faults before they occur by flagging deviations from expected trends. Prevention of major failures is essential to avoid energy waste and reduce downtime.

In conclusion, machine learning (ML) is vital for the future of many IoT systems. It provides the core functionality of these systems, leveraging the huge amount of data collected by devices. No matter how efficient and advanced the hardware is, without machine learning techniques, reducing energy consumption will be always a big challenge.