AI gaining speed in power systems and facilities management

An artificial intelligence-generated illustration of a robot holding its hands out in front of itself while projecting an image that looks like a map. The robot is looking toward buildings that are industrial in appearance.

This image was created using Adobe Firefly, a generative artificial intelligence application.

The past several years have seen rapid advances in the development of artificial intelligence (AI) as more companies and industries realize the capabilities of this powerful technology. A couple of areas in which machine learning (ML), a subset of AI using algorithms to evaluate massive datasets, has shown powerful gains include the electrical utility and facilities management industries.

Electrical power systems are becoming more complex with the rapid addition of various distributed generation (DG) systems requiring utilities to support multidirectional flows of electricity. AI technology can help manage and control electrical utility grids, matching variable energy supply with rising or falling demand and integrating various renewable energy DG sources into the grid. One of ML’s key values is in supporting the growth of smart grids, with the multitude of data points produced by smart meters and other devices monitoring the grid power flows and DG. ML algorithms can also detect the best times to store energy, when to release energy and how much to distribute.

One of the most common uses of AI in the energy sector has been to improve the predictions of energy supply and demand. In conjunction with historic demand data, the number of available generating sources (type and capacity) and forecast weather data, AI networks can predict future electrical output with greater accuracy.

AI can also help with the predictive maintenance of physical assets to prevent grid failures, increasing system reliability and security. ML can analyze large amounts of data from usage statistics, weather data and historical maintenance records to predict potential equipment failures. In addition, AI can integrate data from hazards, such as extreme storms or fires, then adjust grid operations.

With respect to facilities management, AI provides a number of benefits for building owners and their operations and maintenance staff, including optimizing operations, improving decision-making and reducing costs. Again, one of the keys is AI’s ability to provide data-driven insights. Analytical tools powered by AI can evaluate massive amounts of data from numerous sources, including maintenance logs, energy demand and consumption records, past projects, Internet of Things sensors and facility occupancy data.

Decreasing energy use is one of the largest opportunities for facility cost savings. By analyzing granular demand and consumption data from submeters and power monitoring systems and managing indoor environmental conditions based on occupancy, air quality, external temperatures and lighting requirements, AI-driven building automation systems can drive cost savings for the owner or property management company.

Similar to the advantage of monitoring physical grid system performance for utility companies, AI’s analysis of historical facility data can help predict equipment failures and preventative maintenance needs, leading to improved operational efficiency, reliability and cost savings.

The use of AI incorporating ML algorithms is projected to continue its expansion throughout numerous industry sectors in the years ahead. To learn more about Hanson’s efforts in employing AI in our services for clients, contact Robert Knoedler at or Bill Bradford at

Posted on May 15, 2024