As part of our strategic collaboration with BootstrapLabs, we recently co-hosted an Applied AI Insiders Series event in San Francisco, focused on the energy system with speakers that included leading experts in the field. Here we look at how AI technologies are already being used within the energy industry and summarize why we see AI as foundational in future energy system.
In May 2017, Bill Gates wrote an open letter to college graduates offering some career advice. In it, he said if he was starting out today, he’d consider three fields to enter: artificial intelligence, energy and biosciences. On artificial intelligence (AI), he said: “We have only begun to tap into all the ways it will make people’s lives more productive and creative.” On energy, he said: “because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.”
We couldn’t agree more, and we believe that AI has a significant role to play in creating that clean, affordable and reliable energy future. It’s at a very early stage but AI has the potential to revolutionize how we produce, consume and transmit energy; simply put, in the smart grid, AI will be key.
Taking a step back, the energy system of the future is created by four macro-trends: decentralization, decarbonisation, democratization and digitzation. We’re already beginning to see the rise of the prosumer; ordinary people becoming both producers and consumers of energy, through solar production for example. At the same time the grid is becoming less of a one-way street and increasingly an interconnected web of, solar panels, battery storage units, electric vehicles and other internet of things (IoT) devices. It’s anticipated that there will be 1.5bn connected IoT devices by 2020.
All of this is creating more volatility and more complexity in the energy system; an energy system that is still largely controlled manually. Volatility, because renewable energy from natural sources is more unpredictable, and complexity, because of the sheer number of devices that are connected to the grid. Against this backdrop, grid management will need to become more automated with data continuously collected and analyzed, with smart decisions being made on how, where and when to allocate energy most effectively and efficiently. Essentially, AI will become the brain of the grid.
The application of AI in the energy space is very much in focus for our Silicon Valley team, which drives much of our activity looking at the disruptive business models and technologies that have the potential to revolutionize the traditional energy world. Our focus on AI here led us to partner with and invest an initial sum of $5 million in leading AI focused venture capital firm, BootstrapLabs in October. Together with BootstrapLabs, we are working globally to build the largest AI community for energy ecosystems and provide a combination of capital and support to Applied AI start-ups.
As part of our strategic collaboration we recently co-hosted the latest BootstrapLabs’ Applied AI Insiders Series event in San Francisco, which was focused on the energy system. Here, a panel of experts, including: Ben Levy, Co-Founder at BootstrapLabs; Michael Wara, Director, Climate and Energy Policy Program, Woods Institute for the Environment, Stanford University; and Prateek Chakravarty, Head of Worldwide Sales, Bidgely, as well as our own Sebastian Niestrath and Thomas Birr, discussed how AI will change the entire energy value chain; from generation, to distribution, to retail.
AI in the grid
Our current focus here is on AI for predictive maintenance and image recognition for self-checking and self-maintaining grid infrastructure that reduces inefficiencies, improves resilience and improves safety. For example, using AI to interpret data from sensors and predict failures of substations, before they occur.
We recently completed an investment in Sterblue, which builds software that helps drones inspect power lines and wind turbines automatically. The software guides drones along trajectories that wrap tightly around structures, finds anomalies from the collected images and outputs reports. The whole process is automated and triggered at the click of a button using off-the-shelf drones, doubling productivity as compared to manual solutions, while improving reliability.
In the longer-term, our vision is of a fully autonomous digital grid, entirely managed and maintained by AI.
AI in retail
AI is already being deployed in smart home solutions that learn customer behaviors and then manage your home accordingly. An example of a company we invest in in this space is Bidgely. Bidgely is a cloud-based SaaS solution targeting the $6+ billion energy analytics market with its growing portfolio of residential and small/medium business solutions for electric and gas utilities.
Using machine learning algorithms, Bidgely’s patented energy disaggregation technology detects and extracts appliance fingerprints from smart meter data. The data is converted into itemized energy bills to provide useful insights to the user about energy consumption.
Long-term we see AI being deployed to manage peer-to-peer energy trading between neighbors and within microgrids, essentially making trading decisions whether to buy, sell or store electricity based on your energy consumption patterns.
At the innogy Innovation Hub we believe that AI will lead to new products and data driven business models that will fundamentally redefine the energy sector. We lead the charge towards that by working with organizations, like BootstrapLabs, that share our vision, and by looking for the companies that have the potential to create that future.