Artificial Intelligence is the buzzword across every industry now. It can become a pivotal technology to improve energy efficiency, productivity, and outcomes in the energy industry. The power of artificial intelligence lies in its ability to process humongous amounts of data and build interesting insights transforming the day-to-day functioning of the energy industry.
Use Cases of Artificial Intelligence in The Energy Sector
Energy sector offers immense opportunities for exploring the power of AI like complex data analysis, pattern recognition, demand forecasting, and optimization. According to McKinsey & Company, AI and digitization can boost asset productivity by 20% and slash maintenance costs by 10%.
Some of the potential implementation of AI in energy sector can be:
Demand Forecasting
AI can look into data very intricately that traditional energy demand forecasting models may overlook and handle multiple variables parallelly. It can identify patterns from the data and improve accuracy over time.
Several factors influence energy demand forecasting like historical energy consumption data, weather data, calendar data, and economic and demographic data.
AI can sift through these data to detect patterns and identify how the patterns of consumption change depending on them. Accordingly the energy companies can prepare themselves for production.
Renewable Energy Output Forecasting
Energy sector is shifting towards adopting green energy solutions like solar and wind energy. However, these are intermittent energy sources and depend on the weather conditions.
This can become challenging to grid operations because energy has to be managed in real-time. Using AI to accurately predict the availability of renewable energy will help prepare the grid operations.
Increased Production
AI along with machine learning can ramp up productivity for the energy sector. Oil and gas companies are taking the help of machine learning, an AI driven technology, to identify proper places for setting up a rig after analyzing seismic data.
AI can also help to identify the consumption and delivery patterns of oil across a supply chain and automate the processes. This in turn can helps to maintain the balance of demand and supply and save money and resources.
Smart Grids
Grids are converted to smart grid through the integration of sensors, data analytics tools, energy storage systems, and energy management platforms.
Smart grids allow collection of energy usage data of every single device on the grid. This information can be used by AI models to identify a pattern in energy flow and consumption in real-time. Automated demand response systems can be built to turn off energy during peak hours leading to energy savings both for the consumer and company.
Energy Trading and Pricing
Energy market pricings are volatile in nature. Analyzing historical data and weather patterns can help energy traders to make an informed decision on when to buy or sell energy.
AI can also be used to forecast future energy price and identify opportunities from price fluctuations. Hybrid forecasting models can built to automate such processes and bid on triggers.
Predictive Analytics
Predictive analytics can be used to forecast future energy demand and predict when equipment is likely to fail. This information can be used to plan for future infrastructure needs and prevent unexpected outages.
Forecasting future energy demand can help energy companies to build the necessary infrastructure to meet future needs. This can include building new power plants, expanding transmission lines, and installing smart meters.
Predicting when equipment is likely to fail can help energy companies to prevent unexpected outages. This can be done by scheduling maintenance work before a failure occurs. This can save money by avoiding unplanned maintenance work and the cost of replacing critical equipment.
Predictive analytics is a powerful tool that can help energy companies to improve their operations and save money. As the energy industry continues to evolve, predictive analytics will become even more important.
Preventing Power Theft
Electricity theft and fraud is a global problem that costs the energy industry billions of dollars every year. In the United States alone, it is estimated that electricity theft and fraud costs the industry $6 billion annually.
Power theft is the illegal tapping of energy from the grid. This can be done by bypassing meters, tampering with meters, or stealing electricity from underground cables. Energy fraud is the intentional misrepresentation of energy data or energy usage. This can be done by falsifying meter readings, submitting false bills, or using multiple meters to bill for the same usage.
AI and machine learning can be used to automatically detect these anomalies and flag them for energy companies to resolve. This allows energy companies to protect their assets, reduce energy waste, and save money.
Here are some examples of how AI and machine learning are being used to detect electricity theft and fraud:
- Analyzing historical energy usage data to identify patterns that are indicative of theft or fraud.
- Using sensor data to detect anomalies in energy consumption.
- Using machine learning algorithms to classify suspicious activity.
By using AI and machine learning, energy companies can significantly reduce the amount of electricity theft and fraud that occurs. This can save them billions of dollars each year and help to ensure that the energy grid is secure and reliable.
Conclusion
AI will significantly contributes to improving the efficiency of the energy sector by improving operational efficiency, increasing speed of operations, and cutting costs. With increased efficiency the grids will become closely aligned to the needs of the end users.
