Using Machine Learning in the Energy Sector

Machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to imitate the way that humans learn. The “learning” aspect of machine learning refers to the system’s ability to improve its accuracy and knowledge set over time based on experience.

Machine learning has a lot of practical applications, but one of the most powerful ways this technology is being used today is in big data analytics.

Applying machine learning algorithms to generating, aggregating, and analyzing large and complex datasets provides several key benefits, including:

  • Improving process efficiency
  • Reducing need for human intervention
  • Scaling for big data applications
  • Performance monitoring for sensors in remote, hazardous, or unmanned locations

 

How Machine Learning Is Currently Being Used in the Energy Sector

Data analytics in general, and machine learning specifically, is proving to be a valuable asset in the growth and management of the energy sector. 

With a widespread shortage of skilled labor, increased connectivity and reliance on smart technology, and a push for more sustainable and cost-effective energy sources, machine learning will play an integral role in shaping the future of the energy industry.

Here are seven ways the energy sector is harnessing the power of machine learning as part of a data analytics strategy to drive performance improvements and increase ROI:

Predictive Maintenance

Machine learning makes predictive maintenance possible by analyzing historical data and real-time data across multiple sources to predict which systems and parts are likely to fail and when. Creating AI-driven predictive models to monitor the condition of equipment allows maintenance teams to proactively schedule repairs and replacement of vulnerable components and systems.  

By addressing potential problems before they occur, predictive maintenance helps reduce the number of failures and breakdowns, which increases system availability, cost savings, and customer satisfaction. 

 

Grid Management

One of the big challenges of managing power grids is that power generation and power demand need to be equivalent at all times. Otherwise, utilities risk blackouts from insufficient energy on one end and wasted capacity on the other. Machine learning can help maintain balance and increase resilience, especially for renewable energy grids. 

For example, machine learning algorithms can identify changes in usage patterns, which allows utilities to quickly redirect stored energy to areas where it is needed most while decreasing load in regions with lower demand. 

 

Demand and Load Forecasting

Machine learning algorithms make it possible to analyze a variety of influencing factors from disparate sources, such as historical demand, temperature, time, wind speed, weather patterns, and day of the week. 

With the ability to compare analytics from many sources and weather models, utilities can make more accurate predictions about future load and demand requirements, which reduces the amount of capacity they have to hold in reserve “just in case.”

 

Reduced Energy Consumption

Smart metering, a technology that uses machine learning to track energy usage patterns over time, is an effective way to reduce the amount of wasted energy and save money. Machine learning algorithms can analyze data down to the device level and identify which business systems, appliances, and even recurring activities consume the most energy so steps can be taken to improve efficiency and reduce waste.

The Future of Machine Learning in the Energy Sector

The energy sector is at a crossroads, with reliance on fossil fuels slowly giving way to increased usage of renewable energy sources. As this transition plays out, artificial intelligence and machine learning technology will play an integral role in several key use cases.

 

Reliable Renewable Energy Forecasts

Machine learning algorithms can accurately predict the amount of electricity a wind turbine or other renewable energy source can generate during a given time period. This knowledge makes it possible for utilities to forecast supply versus demand with a high level of confidence.

 

Drone/Image-Based Damage Detection

Drones are revolutionizing how utilities monitor and detect damage to transmission and distribution infrastructure in remote or dangerous regions. However, the increased volume of images generated by drone-based detection makes non-AI-augmented review cost- and resource-prohibitive.

Machine Learning Is Powering Growth and Sustainability in the Energy Sector

The energy sector is in transition, and machine learning is helping organizations streamline the process. Smart technology, including sensors and IoT devices, are ushering in the big data era, and generating plants, utilities, and renewable energy providers are harnessing the power of AI to make that data usable.

Related Articles

Meet Manuel Griego: Director of Customer Success

using ai applications in business

Exploring the Advantages of AI Applications in Business

AI-Powered Maintenance for Battlefield Readiness and Logistics

Virtualitics Secures $46 Million+ Contract to Deliver Artificial Intelligence Solutions to Increase Mission Readiness on U.S. Air Force Weapon Systems

Leveraging AI-based asset management system

How to Leverage AI-based Asset Management Applications

Meet Anubha Bhargava: Data Scientist

Virtualitics Wins 2024 Globee Awards for Innovation

Recognized for New AI-Powered Maintenance Decision Intelligence Application, AI and ML Technology, and CTO of the Year PASADENA, Calif., July 8, 2024 — Virtualitics, a

Meet Manuel Griego: Director of Customer Success

Our Meet the Team series showcases the brilliant and talented experts at Virtualitics who are dedicated to empowering organizations with AI-driven analytics and applications. “It’s

using ai applications in business

Exploring the Advantages of AI Applications in Business

Every business is looking for ways to increase productivity, find hidden opportunities, and drive growth. This is the potential that Artificial Intelligence (AI) brings to

AI-Powered Maintenance for Battlefield Readiness and Logistics

John “Mike” Murray is a retired United States Army General, the first Commanding General of United States Army Futures Command (AFC), a four-star Army Command

Virtualitics Secures $46 Million+ Contract to Deliver Artificial Intelligence Solutions to Increase Mission Readiness on U.S. Air Force Weapon Systems

Multi-Year USAF Contract Underscores Company’s Commitment to Enhance Operational Readiness PASADENA, Calif, Oct. 22, 2024 –– Virtualitics, the Mission AI company, today announced the award of a

Leveraging AI-based asset management system

How to Leverage AI-based Asset Management Applications

Faced with limited resources and complex datasets, leaders across industries are searching for ways to increase efficiency, optimize systems, and maintain uptime. The data needed

Meet Anubha Bhargava: Data Scientist

Our Meet the Team series showcases the brilliant and talented experts at Virtualitics who are dedicated to empowering organizations with AI-driven analytics and applications. “Our

Virtualitics is now an awardable vendor for the Tradewinds Solutions Marketplace. Learn more about procuring IRO Maintenance through Tradewinds.