SAN FRANCISCO, Feb. 11, 2019 /PRNewswire-PRWeb/ — Gridium announced today its partnership with The Swig Company, owner and operator of landmark real estate assets across the county. The Swig Company lowers energy use & costs and streamlines sustainable building operations by investing in Gridium technology powered by Artificial Intelligence (AI). Real outcomes include satisfying workspace environments for tenants, accurate Measurement & Verification analyses, and validation of successful energy efficiency projects.

Machine Learning Techniques Streamline Sustainability

“We know we’re achieving our goals when, first and foremost, tenants are happy with our buildings’ workspace environments,” said Deborah Boyer, The Swig Company’s newly named Director of Innovation and Community Impact. “Gridium’s machine learning baselines for those assets’ energy use patterns are examples of how AI is shaping the future of our industry.”

A building operator equipped with Gridium technology doesn’t have to waste time scrolling through plots of BMS data to spot inefficient building drift. Machine learning algorithms automatically update based on the latest building meter & weather data, enabling Gridium software to perform load decompositions and to alert the operator if the lights or HVAC equipment were left running overnight. Because the AI autonomously identifies and communicates anomalous changes in energy use patterns, building operators can address issues proactively. This not only improves tenants’ workspace experience by initiating repairs before complaints occur, but also reduces utility consumption and operational workloads.

Innovative Approaches for Historical Assets

“We hold a long-term view when it comes to preserving and maintaining landmark assets in our portfolio,” said Jay Scholten, Vice President of Innovation and Asset Management at The Swig Company. “Smart investments in energy efficiency technology are a key component of our management strategy. Gridium allows us to take advantage of cutting edge technology such as AI to uncover opportunities and validate projects, even in buildings with legacy infrastructure.”

Gridium began developing its technology nearly a decade ago, and its patented machine learning algorithms proved most accurate and most precise in an assessment performed by the Lawrence Berkeley National Labs. The accuracy of these energy pattern baselines enables trustworthy Measurement & Verification reports, allowing The Swig Company to invest in energy efficiency projects with confidence. When tenants turn to Swig for leadership in sustainable building operations, they can know its AI-powered approach produces meaningful results.

About Gridium Inc.
Buildings use Gridium’s software technology to fine-tune operations, lower energy use and costs, and streamline maintenance communication. With easy-to-use mobile and desktop applications, buildings across the U.S. collaborate with staff, occupants, and vendors to boost service levels and transparency. And with the industry’s most accurate and precise energy data analytics, about half a billion square feet of buildings save millions of dollars on electricity, gas, and water utility bills, extend the useful lives of their assets, and reduce their environmental footprint.

About The Swig Company
The Swig Company, LLC is a privately-owned, San Francisco-based real estate operator with an 80 year history of investment, development, partnership and management of commercial real estate properties in major US markets. Founded by Benjamin H. Swig in 1936, The Swig Company is guided by a long-term investment perspective that has proven adept at stewarding investors safely through multiple market cycles. The company’s vertically-integrated operational platform, which includes investment, asset and property management, and leasing expertise, uses flexibility, generational thinking and sensitivity to sustainable practices to stay at the forefront of emerging trends in the commercial real estate industry. For more information, visit: http://www.swigco.com.

SOURCE Gridium