The U.S. Energy Information Administration (EIA) reports that as of 2020, 102.9 million smart meters had been installed across the United States. By some accounts, each smart meter produces 400 megabytes of data per year. Do the math, and that’s a staggering 41 petabytes of information from smart meters alone – and that’s only a fragment of the total volume of projected data utilities will generate from smart devices. As the grid is modernized with smart sensors and other smart grid technologies, those devices will also produce massive amounts of data. Big data, indeed!
To make the most of all that data, the energy industry is turning to artificial intelligence (AI) and machine learning (ML). The result is better grid management, more efficient operations, and improved grid resilience and reliability. Here’s how.
Artificial Intelligence and Machine Learning Basics
Before understanding how AI and ML can benefit the energy industry, let’s talk about what they are. Artificial intelligence refers to computer systems programmed to think, learn, and problem-solve like humans. Machine learning is a subset of AI, where the computer system uses mathematical models to learn independently. The algorithms used by AI and ML can analyze massive amounts of data from disparate sources producing actionable information for utilities at a speed that humans cannot achieve.
Planning and Forecasting with AI and ML
AI and ML can assist utilities with many aspects of planning and forecasting. First, the technology can create a data-driven maintenance schedule. AI and ML can use predictive models, pattern recognition, and weather data to flag potential equipment failures due to wear and tear before they happen. The utility improves grid resilience and reliability by allowing the utility to fix an issue before a piece of equipment fails.
Analyzing when and how consumers use energy also allows utilities to anticipate energy demand better. This process allows them to create more efficient demand response programs and plan future infrastructure based on current and projected needs.Â
AI and ML weather models can also use historical, real-time, and forecasted weather data to project the output of renewable energy resources. These actions will be critical as wind and solar assets are integrated into a decarbonized grid.
AI and ML Bolster Cybersecurity Efforts
Cybersecurity is top of mind for the energy industry. As hackers become more sophisticated, AI and ML can be used to defend a utility’s hardware and software. AI can encrypt computer systems to ensure security. It can also monitor video cameras positioned near critical infrastructure such as transmission and distribution stations, alerting the authorities immediately in case of trouble.
AI and ML Improve Safety
AI could also improve the safety of line workers and others performing high-risk tasks. For example, self-driving robots could inspect high-power transmission lines or survey underwater cables. Many exciting possibilities exist in this area, especially for those involved in offshore energy development.
AI, ML, and the Future
Big data is here to stay, but the energy industry must overcome potential challenges on the road to a smart grid. There may be some regulatory issues as these new technologies are rolled out, prompting the need to rethink energy market rules. Labor shortages are also a potential issue for the industry. Energy companies will need employees equipped with the requisite skills to manage and expand the capabilities of AI and ML systems. This process may include retraining current employees and partnering with educational institutions to create a pipeline of skilled workers.
