Big data analysis gives Boston a better energy modeling strategy
- By Susan Miller
- Feb 25, 2016
Many city governments have instituted greenhouse gas emission reduction targets in an effort to save money and increase their energy resilience. In Boston, city officials are aiming to reduce emissions 25 percent by 2020 and 80 percent by 2050.
Meeting those targets will require considerable work, which starts with finding out how much energy is currently used across the city. That means collecting data on energy consumption for every building for each hour of the day, all year long.
To crunch data on that scale, the city turned to MIT’s Sustainable Design Lab to build a model that would simulate the energy consumption of the city’s buildings. Researchers could then analyze patterns of energy demand, scope engineering solutions and assess the feasibility of a variety of energy generation solutions.
Using the city’s geographic information systems dataset, the MIT researchers sorted 92,000 buildings into 48 energy profiles and 12 different use categories that represented the energy demand of individual buildings throughout the city. However, the GIS data wasn’t originally collected for energy analysis, so the MIT team had to find ways to work with incomplete data.
After some creative data wrangling, the simulated results were 94 percent accurate for electric consumption and 83 percent accurate for natural gas consumption when compared to Boston’s measured annual energy demand. And the resulting energy usage simulation tool will pay additional dividends. According to MIT News, it can be adapted, rather than reinvented, by others interested in performing similar analyses across the Northeast.
With the baseline findings from the MIT model, the city’s next challenge was to understand what kind of energy conservation solutions would be appropriate, based on the consumption patterns they uncovered. For this analysis, the city turned to researchers at MIT Lincoln Laboratory, a federally funded research and development center. Using a software model called Distributed Energy Resources Customer Adoption Model, which was originally developed to evaluate similar energy solutions for military bases, Lincoln Lab researchers designed custom energy solutions that included smart buildings, local energy generation, district energy or steam networks and microgrids for 42 Boston neighborhoods.
The potential benefits for both Boston and its residents ranged between $600 million and $1.7 billion over the 25-year analysis period. While more complete analysis is needed, the city called the initial indicators very positive.
The results of the research was summarized in the Boston Community Energy Study, released by the city to help stakeholders identify specific project opportunities to reduce costs, greenhouse gas emissions and make Boston's energy system more resilient.
The city will be able to arrange technical assistance to help communities pursue potential projects identified in the study, the through a partnership with the Department of Energy’s Climate Action Champions program that connects property owners with experts that perform no-cost feasibility studies for combined heat and power for district energy loops and microgrids.
The full report is available here.
Susan Miller is executive editor at GCN.
Over a career spent in tech media, Miller has worked in editorial, print production and online, starting on the copy desk at IDG’s ComputerWorld, moving to print production for Federal Computer Week and later helping launch websites and email newsletter delivery for FCW. After a turn at Virginia’s Center for Innovative Technology, where she worked to promote technology-based economic development, she rejoined what was to become 1105 Media in 2004, eventually managing content and production for all the company's government-focused websites. Miller shifted back to editorial in 2012, when she began working with GCN.
Miller has a BA from West Chester University and an MA in English from the University of Delaware.
Connect with Susan at firstname.lastname@example.org or @sjaymiller.