Over the past 100 years, the world’s energy needs have grown drastically with socioeconomic and technological developments. The energy sector is faced with the challenge of scaling energy capabilities to keep up with the rising demand. Improving energy efficiency and meeting consumer demand requires complex operational changes and innovations in technology, infrastructure, and energy management. Artificial intelligence (AI) and machine learning (ML) can provide energy companies with the ability to efficiently and cost-effectively transform how energy is generated, distributed, and stored.

Energy companies must manage a wide range of energy assets to consistently and cost-effectively transmit energy to consumers. AI-integrated energy systems can manage energy assets responsible for producing, distributing, and transmitting energy across the entire grid. It can monitor factors impacting energy consumption in real-time to adjust energy load capacity with a high degree of control. Different regions have different amounts of energy production and consumption. AI can intelligently manage excessive or insufficient energy across different areas: Excess energy from one area can be redirected to other areas with greater demand. Machine learning algorithms can analyze energy production and usage data to determine opportunities for energy companies to buy or sell energy. It can ultimately improve the efficiency of energy distribution which is beneficial for consumers. Overall, AI can optimize energy transfer between energy assets to reduce waste and increase efficient energy usage.

Machine learning can analyze consumer energy data to forecast energy demands and price energy accordingly. It can account for several factors, such as historical usage, weather conditions, dates, periods, etc., to predict energy demand with considerable accuracy. Furthermore, ML-powered analysis can reduce uncertainty in determining energy production from renewable sources such as solar and wind. ML can evaluate weather data from sensors, meteorological forecasts, and satellite data to generate predictions on power generation. In the long term, ML predictions can help energy companies strategically plan future infrastructure to meet market demand.

Efficiently producing and distributing energy requires reliable infrastructure. AI can be vital in monitoring energy systems and infrastructure to determine potential maintenance issues, component failures, and other problems. In some cases, component failures can result in electrocution, fire, and other potentially dangerous situations if not detected and resolved immediately. Furthermore, AI-based tools can improve the scope and breadth of the inspection processes, testing for numerous possible issues. AI can also execute multiple tests simultaneously with excellent accuracy. Insights can also identify instances of energy theft and fraud. ML algorithms can detect and flag suspicious energy consumption and reduce losses resulting from illegal energy consumption. Overall, AI-enabled monitoring helps operational teams to conduct better inspections and maintenance to avoid power outages, service disruptions, and dangerous scenarios.

Around the world, businesses are facing record-high numbers of cyber-attacks, and energy grids are especially vulnerable. Consumers are dependent on reliable and uninterrupted access to electricity, and it is especially critical in areas such as healthcare. Energy girds consist of a complex set of computer systems and software applications, which can be exploited by malicious hackers. Security threats are not only growing in numbers but also in complexity and sophistication, which can prove challenging for security teams to keep up with. AI-integrated security systems allow security teams to rapidly and effectively manage a broad scope of threats and strengthen their overall security. 

Globally, governments and consumers are increasingly focused on reducing greenhouse gas emissions. Energy companies use a host of systems and sensors to track greenhouse gas emissions resulting directly from energy generation and production. However, several factors contribute to the overall carbon footprint that may be difficult to interpret and account for. AI software can collect and track emissions data throughout each step of the value chain. It can provide detailed insights into emissions resulting from each operational process such as transportation, production, distribution, storage, etc. Furthermore, these insights provide a holistic picture of overall emissions that accounts for future demand, emission reduction initiatives, and other relevant factors.