How Does Solar Affect Birds? Deep learning system will monitor birds at solar facilities

15 Jul 2020

By Christina Nunez

When most of us think of birds in relation to clean energy, we immediately picture spinning wind turbine blades. The wind industry invests a great deal of time and money to help mitigate threats to local wildlife, specifically of the avian variety. When it comes to solar panels’ effect on birds, though, we’re practically in the dark. The current data collection methods to quantify such effects are time consuming, but the U.S. Department of Energy’s (DOE) Argonne National Laboratory has proposed a solution: using artificial intelligence and advanced cameras to help improve our understanding of how birds interact with photovoltaic arrays.

The lab has been awarded $1.3 million from DOE’s Solar Energy Technologies Office to develop technology that can cost-effectively monitor avian interactions with solar infrastructure. The three-year project will combine computer vision techniques with a form of artificial intelligence (AI) to monitor solar sites for birds, collecting data on what happens when they fly by, perch on, or collide with solar panels.

“There is speculation about how solar energy infrastructure affects bird populations, but we need more data to scientifically understand what is happening,” said Yuki Hamada, a remote-sensing scientist at Argonne, who is leading the project.

Based on the limited data available, a 2016 Argonne study estimated that collisions with photovoltaic panels at U.S. utility-scale solar facilities kill 37,800–138,600 birds per year. While that number is low compared with building and vehicle strikes, which fell hundreds of millions of birds annually, learning more about how and when those deaths occur could help prevent them.

“The fieldwork necessary to collect all this information is very time- and labor-intensive, requiring people to walk the facilities and search for bird carcasses,” said Leroy Walston, an Argonne ecologist, who led the 2016 study. ​“As a result, it’s quite costly.”

Such methods are also limited in frequency and span, and offer little insight about bird behaviors around solar panels.

The new project aims to reduce the frequency of human surveillance by using cameras and computer models that can collect more and better data at a lower cost. Achieving that involves three tasks: detecting moving objects near solar panels; identifying which of those objects are birds; and classifying events (such as perching, flying through, or colliding). Scientists will build models using deep learning, an AI method that creates models inspired by a human brain’s neural network, making it possible to ​“teach” computers how to do those three tasks by training them on similar examples.

In an earlier Argonne project, researchers trained computers to distinguish drones flying in the sky overhead. According to Adam Szymanski, an Argonne software engineer who developed the drone-detection model, the avian-solar interaction project will build on this capability, bringing in new complexities. The cameras at solar facilities will be angled toward panels rather than pointed upward, so there will be more complex backgrounds. For example, the system will need to tell the difference between birds and other moving objects in the field of view, such as clouds, insects, or people.

Initially, the researchers will set up cameras at one or two solar energy sites, recording and analyzing video. They will need to process and classify hours of video by hand to train the computer model.

Because collisions are relatively rare, they could be simulated using an object (e.g., a toy bird), providing the system with initial information to use as training examples. 

Once the model is trained, it will run internally within the cameras on a live video feed, classifying interactions on the fly — another challenge that involves edge computing, where information is processed closer to where it is collected.

“We won’t have the luxury of recording a lot of video, sending it back to the lab and analyzing it later,” Szymanski added. ​“We have to design the model to be more efficient, so it can be executed in real time at the edge.”

The technology to tackle this real-world challenge may be advanced in the future by leveraging the Sage Cyberinfrastructure initiative, led by Northwestern University, and Argonne’s Waggle sensor system. This would provide a faster, more powerful edge computation platform and multidisciplinary software stack.

The Argonne project was selected for the Solar Energy Technologies Office Fiscal Year 2019 funding program, which includes funding to develop data collection methods to assess the impacts of solar infrastructure on birds. A better understanding of avian-solar interactions can potentially reduce the siting, permitting, and wildlife mitigation costs for solar energy facilities. Several solar energy facilities have granted permission to collect video and evaluate the technology onsite.

To assure sound technology development, the team will also have at its disposal a technical advisory committee, comprised of machine-learning experts from Northwestern University and the University of Chicago, as well as solar technology and avian ecology experts from the Cornell Lab of Ornithology, conservation groups, the solar industry, and governmental agencies.

At the end of the project, Argonne will have developed a camera system that can detect, monitor, and report bird activities around solar facilities. The system will also notify solar facility staff when collisions happen. The technology will then be ready for large-scale field trials at many solar facilities, said Hamada.

The resulting data could be used to detect patterns and begin answering key questions: Are certain types of birds more prone to strikes? Do collisions increase at certain times of the day or year? Does geographic location of the solar panels play a role in the types of interactions? Do solar energy facilities provide viable habitat for birds?

The technological framework can also be used to monitor other wildlife by retraining AI with appropriate data. “Once we identify patterns, that knowledge can be used to design mitigation plans,” Hamada said. ​“Down the road, once a mitigation strategy is in place, the same system can be used to evaluate the strategy’s effectiveness.”

 

Christina Nunez works in communications for Argonne National Laboratory, which seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state, and municipal agencies to help them solve their specific problems, advance America’s scientific leadership, and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

Argonne National Laboratory | http://www.anl.gov

The U.S. Department of Energy Solar Energy Technologies Office (SETO) supports early-stage research and development to improve the affordability, performance, and value of solar technologies on the grid. 

SETO | ener​gy​.gov/​s​o​l​a​r​-​o​ffice

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. 

DOE Office of Science | ener​gy​.gov/​s​c​ience

 


Author: Christina Nunez
Volume: 2020 July/August