new digs

California renewable energy infrastructure company opens new Houston office

Pattern Energy, a California-based company with over 150 employees in Houston, revealed its new local office space. Photo courtesy

A company that's developing renewable energy projects has officially opened their new Houston office that will house its 150-person local development team.

Pattern Energy Group LP, headquartered in San Francisco, has moved its Houston operations into the Montrose Collective at 888 Westheimer Road. The new mixed-use complex developed by Radom Capital is home to restaurants, spas, and other retailers.

"We are doubling down on our commitment to Houston with an innovative new office that is designed to foster the collaborative nature of our work to develop some of America's most ambitious clean power projects," says Hunter Armistead, CEO of Pattern Energy, in a June news release. "Leveraging Houston's top-notch energy workforce has been an important component of our success and we look forward to tapping the City's talent base for our continued growth.

Pattern Energy, which develops and operates wind, solar, transmission, and energy storage projects, has a portfolio of 36 renewable energy facilities that have an operating capacity of nearly 6,000 megawatts across the United States, Canada, Japan, and Mexico.

"This new space will help foster the ingenuity of our dedicated employees and their passion for our mission – to transition the world to renewable energy," Armistead, who's based locally, continues.

Hunter Armistead, CEO of Pattern Energy, celebrated the company's new office last month. Photo courtesy

The company's development team is based in Houston and is currently working on the SunZia Transmission and Wind project in New Mexico and Arizona, which Pattern Energy describes as "the largest clean energy infrastructure project in U.S. history."

Also housed in the new office is the company's Operations Control Center, which provides 24/7 remote monitoring and control of Pattern Energy's renewable energy facilities. Other employees in the new space work on the meteorological, transmission, and energy trading teams.

"We chose the Montrose neighborhood based on our employee feedback," says Cary Kottler, chief development officer, who's also based in Houston. "To achieve our mission, we need to be energized – and Montrose has the vibrancy and atmosphere we were looking for. As we move forward with building a pipeline of truly exciting renewable energy projects, we are confident that this is the ideal location for our employees to write a new chapter in our history."

The new office lobby features a mural by local Houston artist DUAL.

The new office is in Montrose, a neighborhood that had the "vibrancy and atmosphere" Pattern Energy was looking for. Photo courtesy

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A View From HETI

A View From UH

A University of Houston team looked into what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation. Photo via

A team of researchers at the University of Houston are using machine learning to help guide pollution fighting strategies.

As reported in the journal Environmental Pollution last month, the team used the SHAP algorithm of machine learning (a game theory approach) and the Positive Matrix Factorization to pinpoint what areas in Houston had the highest impact on emissions and how certain meteorological factors play into ozone formation.

The paper was authored by Delaney Nelson, a doctoral student at the Department of Earth and Atmospheric Sciences of UH, and Yunsoo Choi, corresponding author and professor of atmospheric chemistry, AI deep learning, air quality modeling and satellite remote sensing.

The team's research closely tracked nitrogen-based compound and volatile organic compound measurements from Texas Commission on Environmental Quality's monitoring stations in the Houston area. After importing measurements from The Lynchburg Ferry station in Houston's ship channel and the urban Milby Park station, the machine learning and SHAP analysis showed a chemically definitive difference between the two areas.

For example, at the industrial station, the most impactful sources of pollution were from oil and gas flaring/production. At the urban site n_decane and industrial emissions/evaporation had the most impact on ozone.

According to Nelson and Choi, this shows that the machine learning and SHAP analysis approach can be used to tailor more precise air quality management strategies in different areas based on the site's unique characteristics.

“Once we know the specific emission sources and factors, we can develop targeted strategies to reduce emissions, which will in turn reduce ozone in the air and make it healthier for everyone," Choi said in a statement.

“Pollution is a critical issue in Houston, where you have extreme high heat and high concentration of ozone in the summers. The types of insights we got are very useful information for the local community to develop effective policies. That’s why we put our time, effort and technological expertise into this project," he continued.

Next the team envisions applying their approach in different cities and across the country.

“Austin, San Antonio and Dallas all have different characteristics, so I expect (volatile organic compound) sources will also be different,” Choi said. “Identifying VOC sources in different cities is very important because each city should have its own unique pollution fighting strategy.”

This summer, the City of Houston released an updated report on its major strategies to combat climate change and build a more resilient future for its residents.

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