By 2027, global Artificial Intelligence (AI) software spending in power and utilities is forecast to reach $17.8 billion. Is your organization deploying AI to solve the complex challenges you face? Let’s take a look at how corporations in the energy sector are using AI to solve their most intractable challenges.
Enable Your Workforce with Generative AI
You’ve heard the buzz about GenAI. But have you enabled your workforce to employ it? GenAI is the subset of AI that uses Large Language Models (LLMs) to produce text, images, or videos. These models learn the underlying patterns of their training data to produce new patterns.
GenAI is driving a revolution in worker enablement through virtual assistants. You can use GenAI for meeting minutes, email summaries, and creation of text, images, and videos. Your organization can use GenAI assistants to analyze secure, internal company data. GenAI analyzes smart meter data, weather forecasts, and grid data to reduce energy costs and carbon emissions. GenAI elevates your organization’s productivity by performing complex tasks and speeding up day-to-day tasks.
Make Full Use of Your Data Using Machine Learning Models
GenAI isn’t the only type of AI that Energy corporations are using. They’re using Machine Learning (ML), as well. Energy companies have a preponderance of data—from facilities, sensors, meters, the grid, and more. AI converts that data into intelligence using Machine Learning (ML) models. ML models are computer algorithms that learn from data without explicit programming. They make predictions and decisions based on patterns they identify in the data.
How can your organization implement ML? Your company’s technical experts can develop their own ML models using Automated Machine Learning (AutoML) software, like DataRobot or Dataiku. Using AutoML, an expert prepares a dataset with a prediction target in mind. For example, an expert might have a dataset of phrases from internet blogs, and their target is to determine whether a phrase is positive or negative. AutoML trains an ML model on the provided data and calculates the most likely outcome for new data points.
What are some uses of AutoML for the energy sector? Experts in the oil and gas industry use AutoML to predict reservoir performance. They also use AutoML to determine when to perform equipment maintenance. AutoML leads to more accurate predictions and better operational efficiency.
AutoML also drives cross-functional innovation in the energy sector. Some energy companies now have “innovation pods” of cross-functional teams that combine chemical engineers, geophysicists, and geochemists with data scientists. These teams work together to develop AI applications. Innovation pods might be just the thing to give your organization a competitive edge.
AI Solutions to Solve Complex Energy Challenges
While AutoML can solve many challenges, custom AI solutions are needed for more complex challenges. These solutions combine different types of AI, like Geospatial AI (GeoAI) and Physics ML.
GeoAI is the subset of AI that processes location-based data to gain insights. For example, energy companies use GeoAI to visualize the subsurface to identify reservoirs. They also use GeoAI to extract resources while minimizing environmental impact.
Physics ML is the subset of AI that integrates traditional physics laws with AI. Physics ML applies machine learning algorithms to real-world physical interactions and measurements. Energy corporations use Physics ML to implement powerful simulations that optimize assets. They use Physics ML simulations to design new facilities and sequester CO2 emissions. Physics ML also enables powerful grid simulations of wind, solar, oil, gas, and hydropower.
Both GeoAI and Physics ML solve problems more efficiently, leading to cost savings. With these powerful technologies, experts solve problems that in the past might have taken a lifetime. The AI revolution is effectively providing workers with their own teams of virtual workers. With these virtual workers, they can solve the complex challenges faced by the energy industry.
Data Centers Giving Power Back to the Grid
AI is critical to the progress of the energy sector. Likewise, energy is critical to the progress of AI. The high computational demands of AI require large data centers. These data centers are becoming energy hubs. AI companies are forward leaning in their interest in decarbonization of their data centers. With onsite energy generation and storage, the data centers are able to give power back to the grid when not needed.
Energy and AI are intricately bound. With their use of GenAI, AutoML, GeoAI and Physics ML, energy corporations are at the AI forefront.
How Can Spark Thought Help You Embrace the Future?
Spark Thought provides solutions that address critical needs in regulatory compliance, operational excellence, and knowledge management. Partnering with Spark Thought helps prepare your organization for the future, with all of its exciting possibilities. Ready to harness AI for efficiency and innovation? Contact Spark Thought today to explore tailored solutions.