Artificial intelligence (AI) is transforming our everyday lives from health care to delivery services to fast-food ordering; the energy sector is no exception. Simply put, AI employs computer programs and machines to solve problems and make decisions that previously required human response.
If you have a smart thermostat in your home for your air conditioner and heater, you’re likely already using AI. The thermostat has the ability to perceive the environment (indoor temperature) and make a decision (should it be hotter or cooler?), which results in a rational action by the AI agent (automatically lowering or raising the thermostat setting). Beyond the homeowner setting a schedule, some smart thermostats go further by using AI to predict behaviors (away-from-home hours for example) or auto features that help you find ways to save energy.
On a bigger scale, how is the energy sector utilizing AI and how does it impact customers? Read more about the current uses of AI and machine learning and what it holds for the future of energy.
AI and Power Grid Management
Most people don’t think about power grids until there’s a problem, like a widespread outage due to bad weather. Power grid managers, like ERCOT in Texas, predict the state’s energy supply and demand. Artificial intelligence is currently utilized and undergoing further advancement to “predict system overloads, energy demands, and possible power failures. This can help companies be ready for changing conditions and better equipped to save power and cut costs” (source AI solutions provider DT40).
Further, intermittent energy sources like wind and solar are growing, but are not always reliable or available when needed. AI and machine learning can provide better predictions for these renewable resources and more efficiently manage energy grids with that data in mind. For grid maintenance, robots are improving installation, turbine and pipeline repair, and other infrastructure that reduces costs.
According to Energy Digital, “Grids can now be integrated with sensors, data analytics tools, energy storage systems, energy management platforms, and other types of energy technology to become ‘smart’.” A smart grid, as opposed to traditional networks, “is the implementation of AI, Cloud, and digital technologies that support control and self-regulation” (source N-ix).
With smart grids, data can be collected from each device along the energy grid to monitor energy flow and use in real time. As a result, energy efficiency efforts are employed, such as an automated response to turn off energy during peak hours, saving both the homeowner and energy companies. With the increasing development of smart grids comes better preventative and predictive maintenance.
Customer Service and Engagement
Power companies utilize AI to interact with customers, like chat bots to answer questions in real time (instead of waiting for regular business hours) and phone apps that provide daily data on energy usage or a consumers’ account balance for prepaid electricity plans. Data analytics helps homeowners know their energy usage habits and tips on reducing consumption and saving money.
Energy Predictions and Production
Beyond predicting short-term power supply needs, predictive analytics can anticipate future energy demands. In addition to investing in needed infrastructure for the future, AI can predict when equipment is likely to fail, thus preventing unplanned outages and saving money for critical replacements or maintenance in a pinch.
Another outcome of artificial intelligence in the energy sector is Improved production. Machine learning algorithms help oil and gas companies determine the best locations to drill wells, based on seismic surveys and other data. This results in more cost-efficient energy production.
Building new power plants, which have an operating lifespan of 30 to 40 years, can be an expensive endeavor. But ensuring energy is available when needed requires storing energy resources. Smart energy storage systems and AI-powered technologies can provide a more efficient way to store energy and integrate renewable resources that are more difficult to predict. Likewise, a cloud-based “virtual power plant” reduces reliance on coal power while helping energy companies deliver power when their supply is low.
Challenges and the Future of AI Energy
For decades, technology companies have been working to improve AI and machine learning for the energy industry. Implementation costs are an investment, but result in more sustainable practices with less waste or unwanted surprises (like broken equipment or outages due to depleted energy supply). Another concern includes cyberattacks on the energy grid, but AI and machine learning can actually help predict and prevent security breaches.
The benefits of artificial intelligence in the energy sector are evident, for consumers and providers alike. The world’s growing energy demand will benefit from AI’s data analytics, forecasting, resource management (including renewable energy), and failure prevention. As demand swells, so will technological advances that make energy production more efficient, reliable, and affordable.