2 March 2021
Los Angeles, CA


Artificial intelligence in energy and utilities is proving to be a tremendous boon for addressing common efficiency issues through Internet of Things (IoT) devices and smart grid capabilities, resulting in enhanced power management. Beyond operational and cost efficiency, AI solutions for power and energy applications are also generating significant benefits for utility customers, the global economy, and the environment.
According to a Microsoft report in association with PwC, AI in the energy sector is expected to contribute 2.2% to future global GDP by 2030. With these gains in mind, it’s no wonder decision-makers in this sector are seeking AI solutions for the impact they can generate. Here are some ways AI is revolutionizing the industry and paving the way for a higher standard of operation. 
Improving Cost & Operational Efficiencies
Driving value through AI implementation enables power plants to achieve greater cost and operational efficiencies in several key areas that include: 
  • Predictive maintenance 
  • Load forecasting & optimization 
  • Grid reliability 
  • Energy theft prevention 
  • Renewable resource optimization
According to an IEA technology report, the power sector stands to save around $80 billion per year until 2040 with the help of digital technologies. Enhancing these areas via AI solutions results in greater revenue retention, reduced unplanned outages, extended operational lifetime of assets, and boosted power plant and network efficiency.  
+ Predictive Maintenance 
Power plant operators are going beyond preventative-only maintenance and expanding to include predictive maintenance and fault predictions through equipment monitoring and better data analysis. For example, the United Kingdom’s National Grid has utilized drones and image recognition to inspect over 7,000 miles of overhead lines that transmit electricity from power plants to homes and businesses. Whereas before engineers had to assess damage and maintenance themselves, drones make light work of this task by utilizing high-resolution still video and infrared cameras to allow for an equal (if not better) assessment of equipment. Additionally, AI paired with sensors can be used to monitor equipment and identify faults that might have gone unnoticed by the human eye. 
An AI system can then predict failure months in advance through trend analysis and deep learning tactics. Cognitive AI goes one step further to draw explainable insights for power plant operators to act on, thereby saving time and money that would have otherwise been spent on damage control.  
+ Load Forecasting & Optimization 
AI is especially critical for the energy industry to accurately forecast and predict in real-time how much power needs to be produced. Because electricity is a resource that cannot be easily stored, this makes accurate energy consumption a key component of an efficient power plant. In other words, more accurate power predictions mean lower production costs, reduced energy waste, and retained revenue. 
Other important factors to understand include the drivers that affect load requirements such as weather, time of day, calendar days, and seasonality. Due to the unpredictable nature of these influences, power plant operators are implementing artificial neural network (ANN) technologies to leverage historical data to generate accurate predictions for future loads that take these factors and their variability into account. 
+ Enhanced Grid Reliability  
In line with forecasting power supply and demand, AI-powered smart grids enable power plants to integrate a network of devices, sensors, and data that can better monitor every aspect of a power plant’s operation to find areas of improvement and increase resiliency. Having individual components communicate with each other enables grid operators to rapidly respond to changes in energy demand and optimize generation as well as distribution. In other words, facilitating an integrated view of each component allows for better grid management.  
During a natural disaster or unforeseen emergency, for example, a smart grid can allow for automatic electricity rerouting during equipment failure and even contain power outages when they occur to mitigate their effects. By assessing the damage, controlling it, and addressing it, these smart grids can reduce downtime and accelerate their startup time more strategically. 
+ Energy Theft Prevention 
Energy theft can be especially prevalent in emerging markets, Brazil being a prime example. However, energy theft is not an issue limited to Brazil considering energy companies in other countries like Nigeria are seeing as much as 40% of their distributed energy supply siphoned off as a result of tampered meters or tampered overhead cabling.  
AI technology can address this issue by implementing measures such as smart meters to monitor the distribution of energy in designated areas. AI algorithms can also be used to recognize discrepancies in property energy levels and pinpoint areas where suspicious activity is occurring. So, rather than blindly sending engineers to inspect tampered areas—into dangerous locations sometimes—an AI system can identify targeted areas that would benefit most from an in-person inspection.  

AI in Renewable Energy
As mentioned before, load forecasting can be unpredictable, therefore necessitating AI technologies to improve forecast accuracy. This is also the case in the renewable energy sector since it’s reliant on uncontrollable elements such as wind, weather, and water flow, making it equally as unpredictable. In addition to the aforementioned use cases for AI in the utilities sector, it’s this unpredictability concerning renewables that AI can address and is why many are already reaping the benefits from AI in renewable energy production.  
In fact, machine learning algorithms have been able to increase the value of wind energy for Google’s wind farms by 20%. In the case of wind and solar energy, AI can assess the reliability of solar and wind power generation by analyzing meteorological data, historical weather data, and real-time weather forecasts to predict capacity levels. This gives power plants insight into when they need to gather, store, and distribute power during peak periods. The ability to sufficiently and reliably predict energy supply and reconcile this with variable demands increases the flexibility and sustainability of these energy sources, which in turn increases their value.  
On a similar note, hydroelectricity production is seeing extraordinary benefits from AI implementation to help alleviate some of the uncertainty when it comes to changing air temperatures and precipitation levels. Norwegian energy company Agder Energi, for example, conducted a project where they transitioned from using outdated mathematical models for optimizing energy production to using deep reinforcement learning, where the machines can take this uncertainty into account and learn by trial-and-error to optimize production. Faced with growing pressure to reduce their carbon emissions as a result of climate change, AI has become more of a necessity than an optional luxury for many renewable energy suppliers. 
Enhancing the Utility Customer Relationship
From infrastructure optimization to supply and demand balance, AI has drastically impacted how the utilities industry operates and this includes their consumer relationships. Customers’ interests and expectations are dynamic and as such, providers must work to keep up with these changes. Gone are the days where consumers will sit back and remain content with traditional power models according to a US nationwide Deloitte study of residential and commercial utility customers. Nowadays, customers are seeking to reduce the cost of their utilities in addition to increasing transparency between them and their utility providers while also expressing growing concerns for the environment.  
This generates a trove of opportunities for providers to optimize the customer experience in line with changing interests or create a two-way communication system for increased transparency between them and their customers. All of which can be made possible through an AI-implemented system. For instance, with a disaggregated energy usage strategy, integrated with an AI system, providers can draw insight from how customers interact with their smart home devices and monitor their energy consumption patterns to personalize products, services, and communications. Alternatively, providers can improve customer understanding of renewable energy resources through AI-enhanced renewable energy demonstration projects.
Exponential growth in emerging technologies and ever-changing consumer expectations for their utility providers are revolutionizing the way utility providers are managing their operations. As artificial intelligence continues to grow more advanced beyond conventional data science and deep learning algorithms, so too will the implementation and use cases for these emerging AI technologies.
The energy sector stands to gain much from AI implementation, bringing in opportunities for enhanced asset maintenance, forecast predictions, and heightened energy efficiency. As of 2020, the US Department of Energy has invested $37 million in AI research and design, contributing to the global momentum that many energy suppliers are faced with to include not just AI, but integrated digital technologies in their operations to create a more efficient power plant. With the ability to boost profitability, productivity, and safety, AI has become the proverbial linchpin enabling power plants to continue operating sustainably and adapt to disruptions in the industry.