How Machine Learning Can Be Used to Cut Energy Bills

Utility companies are turning to machine learning to lower customers’ energy bills — and their own. They can offer better prices for consumers when overhead and operational costs are lower, creating a win-win situation for everyone involved. Here’s how machine learning and AI are making power cheaper for companies and consumers to cut energy bills.

Enabling Predictive Maintenance

In contrast to preventive maintenance — which occurs on a schedule, not necessarily at optimal times — predictive maintenance harnesses the power of machine learning to estimate when equipment will need to be replaced or repaired. Machine learning uses sensors to analyze a system’s past use patterns to detect upcoming issues. It then alerts maintenance staff that they’ll need to perform upkeep soon.

In 2021, 40% of respondents in manufacturing industries reported using predictive maintenance via analytical tools. The technology has major potential for energy savings for utility companies — replacing and repairing faulty equipment on time translates to wasting less power running inefficient machines.

For example, placing sensors in an HVAC system could help identify when the air conditioner isn’t cooling as well as it used to. The sensors could use object identification to notice when a fan breaks or analyze vibration patterns to detect a failing compressor.

Reducing Manufacturing Waste

Sensors can measure the output and efficiency of individual machines within an energy facility. They may all look the same on the outside, but each conveyor belt or turbine could use different amounts of electricity. Replacing or relocating the least efficient ones will lower costs.

Machine learning sensors can also detect inefficiencies in humidity control systems, help managers find the correct-sized drives and motors for equipment, and determine the optimal temperature at which equipment works. They can collect information on all aspects of energy production, such as the types of raw materials used, which time of day machinery is used the most, and the age of different devices. The software analyzes the data to determine if anything is running at less-than-efficient levels.

Utility companies can use this wealth of data to make decisions that will lower their energy bills. These savings are then passed on to consumers.

Generating Energy Use Insights

A smart meter measures power consumption patterns within a specified area. As the software learns over time, it forecasts how best to save energy, modeling its predictions based on when and where people use it most. It can identify power-hungry devices that keep running when idle and use more than their fair share of electricity.

Smart meters installed in company facilities can lower energy bills by eliminating inefficiencies. They allow staff to adjust their habits, replace outdated equipment, and optimally run HVAC systems to reduce electricity consumption.

Optimizing the Grid to Cut Energy Bills

Placing smart meters strategically helps optimize everything from battery storage to home energy use. A smart grid can predict and manage demand throughout the day based on customers’ past energy consumption. It can help people decide when to use stored power and when to sell it back to the grid, reducing waste and lowering costs for utilities and consumers.

Smart grids can also predict the availability of wind and solar energy. This feature helps utilities store and use electricity more efficiently, making customers more confident in renewables.

Using Smart Thermostats and Lighting

Another practice that cuts the cost of running a utility operation and reduces costs for consumers to cut energy bills is using smart technology to manage energy consumption. Smart thermostats can adjust a building’s heating and cooling depending on occupancy levels, season, and time of day. The software learns over time to better manage electricity use.

Similarly, smart lighting uses IoT-connected bulbs to automatically adjust the color and intensity of lights inside a building. They can shut off automatically on a timer or when people leave the room, saving money and making structures more energy efficient.

Supercharging the Energy Industry

As machine learning becomes more powerful, the technology has the potential to revolutionize how utility companies operate. It can identify production inefficiencies and help staff perform predictive maintenance leading to significant savings.

Reducing overhead costs translates to lower energy bills for consumers, making electricity more affordable for everyone. It also lowers utility companies’ environmental impact and contributes to a cleaner, more energy-efficient world, which is a worthwhile goal to strive for.

Originally posted on OpenDataScience.com

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