5 Ways Artificial Intelligence and Machine Learning Help Solve the Power Load Challenge

The nation’s energy infrastructure is in dire need of change. Electricity demands are rising, but aging infrastructure, understaffed energy organizations, and shifting trends make it challenging to fulfill those needs. Power load balancing will be crucial in changing these circumstances, but conventional approaches aren’t sufficient. The answer lies in artificial intelligence (AI). AI and its more advanced subset, machine learning (ML), provide the adaptability, accuracy, and speed modern grids need.

Why Grids Need Better Power Load Balancing

Power load balancing is a pain point for grids across the nation today. Outages have become more frequent and severe over the past 10 years—a trend that will only grow amid rising electricity consumption if grids don’t adapt. While power distribution isn’t the only contributor to these events, it certainly plays a role.

The clean energy transition further raises the demand for more efficient load balancing. The nation must embrace renewables like wind and solar to stave off climate change’s most dramatic impacts, but these sources can’t produce power on demand.

Peak renewable energy generation hours don’t often align with peak consumption. Consequently, grids must adapt to ongoing changes and distribute power to different areas more effectively to prevent waste and make large-scale adoption practical. Conventional infrastructure lacks the flexibility necessary to enable that level of balancing.

How AI and ML Improve Load Balancing

AI and ML offer a solution. AI models analyze data to identify patterns and adjust operations based on these insights. ML can go a step further and learn from ongoing changes during implementation to become more accurate over time. These technologies have several applications for power load balancing.

Adjusting to Real-Time Changes. The most straightforward use case for AI in load balancing is adapting to changing conditions. AI can monitor power consumption and generation data in real time to determine which areas require the most energy at any given time. It can then distribute electricity accordingly to even loads across the grid.

These real-time adjustments are crucial, as many factors can affect output loads, causing equipment to malfunction. Power companies can use AI to balance loads and ensure grids can adapt to unexpected disruptions. As a result, shifting weather, uneven energy consumption, and other changes won’t result in errors or waste.

Other technologies can theoretically adapt to changing conditions, but AI is far more effective. It can understand data and its impact more accurately than other solutions—and certainly more than humans—and responds to these changes faster.

Predicting Future Load Requirements. ML can take these adjustments a step further and predict future demands. Predictive analytics models look at past data to understand how certain conditions correspond to larger trends. They can then accurately predict future shifts and adapt to maintain ideal conditions.

Residential load balancing is an ideal use case. ML models in smart transformers can analyze power consumption data to determine which homes consume the most energy at which times. They can then distribute loads based on this information as peak hours approach, ensuring sufficient outputs and preventing disruption without needing last-minute adjustments.

ML models become more accurate as they encounter more data and learn from real-world trends. Using these ongoing learning models instead of simpler algorithms lets power companies predict future load requirements more reliably and further into the future. They can then prepare more effectively to prevent disruption.

Detecting Anomalies. Anomaly detection is another key use case for AI in power load balancing. Some scenarios are unpredictable, even for the most reliable ML models. Fast responses are crucial to address these unforeseen issues, and AI provides more speed than achievable otherwise.

AI anomaly detection works by learning what normal circumstances look like based on historical data. When real-time information falls outside these normal parameters, the model immediately identifies it as a potential issue. It can then either balance loads to account for the anomaly or—if it’s a larger issue—alert maintenance employees so they can fix it quickly.

These quick responses are ideal for catching and responding to issues like downed power lines, malfunctioning transformers, or similar equipment errors. AI’s speed and accuracy ensure repair teams can respond as quickly as possible to minimize costs and prevent larger outages.

Improving Grid Cybersecurity. Those same anomaly detection features can help make grids more secure. While cybersecurity may seem unrelated to power load balancing to some, it’s an increasingly urgent part of the process. Power grids are experiencing a rise in cyberattacks as they implement more connected technologies, so better security is crucial.

AI-powered continuous monitoring is a must for grid security. As power companies employ more Internet of Things (IoT) devices to improve operations, they introduce more potential entry points for hackers. AI can monitor for suspicious activity in the same way it addresses operational issues.

Continuous monitoring models can isolate a potentially compromised endpoint or part of the grid immediately after detecting the issue. They can then alert cybersecurity specialists to investigate and act further. These quick responses prevent highly disruptive attacks and are only possible through automation.

Enabling Ongoing Improvements. Across all these use cases, ML models provide the insights power companies need to optimize their operations. Energy technology will change, consumption trends will shift, and grids will reorganize. Best practices will likewise evolve, and AI is key to capitalizing on that evolution.

Changes over time will result in differences in data. ML can analyze information to predict future trends or highlight areas where new inefficiencies or opportunities have arisen. This technology can notice changes that are too small for humans to pick up on, informing earlier adjustments.

Energy organizations that gather these AI insights can develop a roadmap for improvement. Regularly reviewing and adapting to these AI-powered suggestions ensures power grids remain as efficient and reliable as possible.

AI and ML Could Revolutionize Power Load Balancing

Power load balancing is a complex process, and it’s never a one-time fix. It requires ongoing adjustments and quick responses. AI and ML excel in those categories.

The power industry will evolve as more energy companies adopt these technologies. AI and ML make load balancing and supporting processes easier and more effective than ever.

—Emily Newton is an industrial journalist who regularly covers stories for the utilities and energy sectors. She is also Editor-in-Chief of Revolutionized.