Buildings and the electricity grid are inextricably linked. The building sector represents up to 75% of all electricity usage and is also a disproportionately large contributor to peak energy demand, according to our recent report. The electricity grid, including power generation, must serve that demand, and to do so efficiently, it must develop a deep understanding of energy demand in our ever-evolving buildings. These two traditional industries are gradually exploring the potential of artificial intelligence (AI) to create efficiency and flexibility for their individual needs, but the true potential lies in the deep interconnection of intelligent energy supply with intelligent energy demand. That potential can only be reached with the convergence of data and energy.
“If you step back for a moment you realize there are two (separate) trillion-dollar industries—the energy industry and the data and information industry—which are now intersecting in a way they never have before,” said Arun Majumdar, Stanford University Jay Precourt Provostial Chair Professor of Mechanical Engineering and founding director of ARPA-E, speaking at an Electric Power Research Institute (EPRI) AI and Electric Power Roundtable discussion earlier this year. “The people who focus on data do not generally have expertise regarding the electricity industry and vice versa. We have entities like EPRI trying to connect the two and this is of enormous value.”
The datafication of energy must happen at a grid-level, where the generation and transmission of electricity can be digitized to provide the data for advanced analytics and AI to feed off in order to create efficiency and flexibility. The datafication of energy must also happen at a building-level, where building systems and occupant behavior can be digitized to provide the data for advanced analytics and AI to feed off in order to create efficiency and flexibility. Only when both have been digitized can we begin to strive for the ultimate efficiency and flexibility of an intelligent interrelationship between buildings and the grid. However, if you thought the building industry was slow to adopt new technology, then you’ll be shocked by the power sector.
“The utility sector by nature is a risk-averse industry, but it’s time to think about how to adapt their business models to embrace new AI technologies,” said Liang Min, Managing Director of the Bits & Watts Initiative at Stanford University. “If utilities dedicate resources to identifying right use cases and conducting pilot programs, I think they will see benefits, and it will eventually lead to enterprise-wide adoption.”
The utility sector is not only being urged to digitize and apply AI in order to advance its relationship with buildings, there are also numerous benefits for the grid itself. On the generation side, the emergence of renewable energy has created intermittent supply that often depends on the weather and other environmental conditions. Digitization in the form of smart grids is required to avoid the inefficiencies of prioritizing these cleaner forms of energy while ensuring stable electricity provision. Energy storage creates flexibility for renewable generation but also adds a new kind of grid element, which adds complexity to power system management on a regional or national scale. Such complexity is best addressed by AI, which requires digitization.
The digitization of power grids also creates new problems, however. As utilities turn to renewable energy and add millions of other components like energy storage and smart meters, they also rapidly multiply the number of digital endpoints, which each create vulnerability to cyberattacks. In February 2016, a cyberattack against the Ukrainian power grid left more than 5 million people without electricity, an October 2020 power outage in Mumbai is also suspected to have been caused by a cyberattack that impacted stock markets, trains, and thousands of households in India’s financial hub. The May 2021 ransomware attack on the US Colonial Pipeline shows that cyberattacks can happen anywhere, but AI can protect our infrastructure.
“AI can be deployed to provide cyber-risk analytics for improving organizational resilience and understanding cyber risk. AI technologies can improve threat intelligence, prediction, and protection as well as enabling faster attack detection and response while reducing the need for human cybersecurity experts. AI can learn from security analysts and improve its performance over time, leading to time savings and better decisions,” explains our recent report – AI & Machine Learning in Smart Commercial Buildings. “These “smart cyber” capabilities are urgently needed as cyber-attacks continue to grow in volume and sophistication.”
Back inside buildings, the benefits of AI are becoming clear and widespread, as discussed in our recent article on the use cases of AI in various building verticals. The hype surrounding the potential of AI in buildings has even driven some market observers to predict that widespread AI-enabled buildings might become a commercial reality within the next five years. However, given that most buildings still do not have the prerequisite IoT and data infrastructure in place to support the automation of individual building systems, our research suggests AI might take a little longer. In the meantime, however, AI can soon be applied to the building-grid relationship.
“Where there is more near-term potential, however, is in the scaling of AI enabled solutions beyond individual buildings, particularly in the field of building energy management. Here credible research is being undertaken to enable a kind of swarm or hive intelligence over a portfolio of buildings and at an urban scale,” reads our 2021 AI report. “Using energy performance data from a larger number of buildings could credibly allow for improved matching of demand and production at the grid level, as well as improved transfer of best practice approaches and optimizations methods between buildings.”
Supply and demand are the two sides of the energy coin. On the supply side power generation, transmission, and distribution can become more efficient, flexible, and secure with the application of AI to a digitized grid. On the demand side, buildings make up the vast majority of electricity consumption and, therefore, are critical to the development of more efficient, flexible, and secure grids, as well as smarter buildings themselves. To address major global issues such as climate change and energy security, as well as driving society forwards with progressive technologies, both sides of the coin must keep up with digitization and AI adoption. The evolution of these two traditional industries is fast becoming a critical bottleneck to the evolution of civilization itself.
“AI is at a point where I believe the technology has advanced to support scaling up adoption. Meanwhile, we know that society depends on electric power 24/7 to run everything from health care and emergency resources to communications infrastructure and in today’s current situation, working from our homes,” said Neil Wilmshurst, Senior Vice President of EPRI’s Energy System Resources. “Reliability and resilience have never been more essential in a time when we’re also making a critical energy systems transition to meet global climate goals and demand needs. AI must be a tool in the toolbox, and the time is now—not tomorrow—to accelerate those applications.”