Managing Real-Life Energy Use With Artificial Intelligence

The Evolution of AI

In its earliest applications for energy management, AI acted like a simple timer. When a set time elapsed or a set time limit was reached, the AI turned specific functions on or off.

Think about your basic digital thermostat. You can set different things to happen at different times. For example, at 6 p.m., set the temperature to 68 degrees. At 10 p.m., lower the temperature to 64 degrees. Raise it back to 68 degrees at 8 a.m. As time passed and technology evolved, newer models offered even more control. Today’s smart thermostats can now respond in real time to temperature changes.

At first, that’s how energy planning and management used AI: like a thermostat. AI turned specific functions on or off, and it detected operational events like equipment failures or sudden increases in energy use. As the AI tools improved, they were able to provide valuable insight by comparing a building’s performance with established standards.

For example, AI can monitor the conditions inside your facility and then maintain a constant temperature using the least possible amount of energy. Research tells us that AI-powered predictive controls can lower energy use by 20% without compromising comfort. If you’re a facility manager looking to reduce energy use across huge production spaces or office complexes, AI makes it possible.

So How Does AI Actually Work?

AI technology has been designed to mimic certain aspects of human intelligence. For example, AI can predict outcomes based on existing data or design plans that optimize resource use. 

You need two things to accomplish this: machine learning and data analytics. Machine-learning algorithms are the foundation of AI. They act like digital brain pathways, allowing AI to imitate some parts of human intelligence. Data analytics allow AI tools to get better and more accurate over time.

One simple example of machine learning is an algorithm that defines the concept of “dog.” It has four legs, a tail, fur, pointy ears, etc. This definition is then applied to large sets of data. The more data used, and the more analyses performed, the more accurate the model becomes—and the better the machine learning becomes at identifying dogs.

Or look at AI-driven chatbots. Using machine learning and data analytics, the chatbot can examine user input and come up with relevant answers. Each user interaction adds more information to the chatbot’s database, and its output improves over time.

How AI Can Benefit Energy Management

Energy Planning and Strategic Development

AI can analyze historical energy data to predict future energy demands and optimize different ways to source that energy. For example, AI can use data from the past five years to anticipate peaks and valleys based on customer demand and operational requirements. This means that you can spend less when energy use is low and then prepare for higher costs when usage ramps up.

AI can also help your company develop energy-management plans that align with sustainability goals and regulatory requirements, such as those set by the International Energy Agency (IEA). For example, IEA’s Net Zero by 2050 road map includes milestones such as electric vehicles representing 5% of global sales by 2030, and solar- and wind-power generation accounting for 10% of total electricity generation. 

Equipment Optimization and Lifespan Extension

AI can help to simultaneously reduce energy use and improve comfort by analyzing factors like your building performance, occupancy, weather conditions, and equipment schedules.

You can use AI to identify when equipment isn’t working normally and to alert facility managers to take action, which lowers the risk of downtime. AI tools also can help create optimized start and stop schedules for equipment, which can save money on energy costs without your productivity taking a hit. 

Predictive Analytics for Energy Consumption

Predictive analysis is the process of using current and historical data to predict future trends. This data is gathered from building automation systems, connected sensors and information stored in the cloud. AI analyzes this data and recommends ways to optimize performance.

Consider a manufacturing plant that runs 24 hours a day, seven days a week. Predictive analytics can create optimized lighting and HVAC schedules to account for staffing differences between day and night shifts. 

AI-Driven Fault Detection and Preventive Maintenance

Equipment failures can happen quickly and without warning. However, many failures can be predicted—and prevented.

These predictions require an in-depth analysis of large datasets to identify trends. The volume and variety of available data make this challenging (if not impossible) for maintenance and operations teams. With AI, however, it’s possible to detect patterns, anomalies and inefficiencies in current operations and anticipate what might happen next. 

This means that your company can predict equipment failures, allowing for a response before small issues become large problems. Operations managers can also proactively schedule maintenance to replace parts or swap out components, which can improve equipment lifespans. 

Energy Efficiency and Sustainability Goals

As energy standards evolve, companies face increasing pressure to comply. The Office of Energy Efficiency and Renewable Energy notes that both states and cities are adopting BEPS to improve building efficiency. Many standards include regulations for energy consumption and emissions. That is, companies need to improve efficiency by a specific percentage by a set date. For example, under its Climate Pollution Reduction Plan, Maryland aims to reduce greenhouse gas emissions 60% by 2031 and then achieve net-zero emissions by 2045.

AI can help companies achieve these goals by continually looking at energy consumption. A company with multiple buildings spread across a large campus can use AI to track how much energy each building uses, when this usage rises or falls, and if any anomalies exist, such as a building that consumes a lot more energy than the others. From there, an AI analysis can help uncover opportunities to reduce energy use and meet BEPS goals. 

Make AI a Valuable Partner in Your Business

AI can help your company improve its operational efficiency and meet changing energy standards. To make the most of AI, you’ll need the right approach. While solutions are improving, technology alone isn’t enough to maximize AI’s impact.

In practice, you need to combine people with processes to deliver the best outcomes. When you create a strategy that leverages AI but doesn’t ignore the human element, you’ll be better prepared to navigate the changing landscape of energy efficiency and regulatory obligations.