Agencies bring energy modeling out of the lab

Buildings in the United States consume 40 percent of the nation’s total energy and are responsible for 40 percent of carbon dioxide emissions, according to the Department of Energy. Increasing building energy efficiency, if widely accomplished, could boost energy and environmental sustainability.

But until recently, calculations required for energy modeling and simulation were beyond most users. Now agencies are taking advantage of greater computer power to build tools that help guide decisions about efficiency.

The National Institute of Standards and Technology recently developed a database and software to help building professionals evaluate whether going beyond the current code would deliver energy savings that exceed the initial investment in energy upgrades.

BIRDS (Building Industry Reporting and Design for Sustainability) is a web-application designed for sustainability performance (energy, cost, and environmental impacts) comparisons for 11 different U.S. commercial building types.  The energy, environment and cost data in BIRDS measures building operating energy use through detailed energy simulations, building materials use through innovative life-cycle material inventories, and building costs over time.

It also includes integrated metrics, gauges sustainability of materials and energy usage, assesses carbon footprints and 11 other indicators of environmental performance, and tabulates economic costs over nine different investment horizons.

NIST's aim is to provide hands-on tools that anyone can use to answer "what if" questions when planning or designing a new office building, retail store, or any of nine other types of commercial structures.

BIRDS will grow more robust and add new capabilities in future versions, first by adding the building types needed to fully represent the nation's stock of 5 million commercial buildings. Coming versions will include new houses and then energy retrofits for existing homes and commercial buildings.

Additional flexibility will be incorporated to give users greater ability to customize the analysis to their specific situation and interests.

A similar, but more sophisticated analysis of energy savings in existing buildings can be obtained with building energy modeling (BEM), which uses computer simulations to estimate energy use and guide the design of new buildings as well as energy improvements to existing buildings.

BEM allows users, such as engineering firms, to adjust a building’s features to meet the needs of owners and occupants while reducing energy bills.

“When modeling a building, you might be simulating for total energy saved after implementing new features. Or you may be optimizing for utility cost savings, or limiting electricity use during peak load periods, or other desired results,” said Jibonananda Sanyal of the Department of Energy’s Oak Ridge National Laboratory’s Building Technologies Research and Integration Center, a DOE user facility that develops new building technologies and provides unique capabilities for evaluating products and whole-building systems for market.

But before using BEM to identify energy improvements to existing buildings, BEM parameters must first be collected, entered into the tool and adjusted so outputs reasonably match past energy usage. This can be a time consuming chore, but it’s often required to receive tax rebates and utility incentives.

ORNL building researchers at the lab’s Building Technologies Research and Integration Center have created  Autotune calibration software, which reduces the amount of time and expertise needed to optimize building parameters for cost and energy savings.

To develop Autotune, the researchers used ORNL’s 27-petaflop Titan supercomputer and the National Institute for Computational Sciences’ Nautilus system to perform millions of simulations for a range of standard building types with generated data totaling hundreds of terabytes.

On Titan, the team has been able to run annual energy simulations for more than half a million buildings in less than one hour using over a third of Titan’s nearly 300,000 CPU cores in parallel, the lab said.

Additionally, they worked with building technology experts to identify about 150 of the most important parameters. By focusing on those, they can reduce computational load while still ensuring highly accurate results. The software uses machine learning algorithms to  learn successful versus unsuccessful paths to optimization.

In this way, if similar building input parameters are introduced later, the software optimizes the results more quickly by cutting out what didn’t work before.

Autotune’s fully-automated process has routinely calibrated models to an error below 1 percent on all building types tested. With such precision, an overnight Autotune process is far less costly than the time it would take an expert to manually calibrate a model.

The team is currently making Autotune capabilities available to a limited set of beta testers through a web service and anticipates making it publicly available in September 2015.

“We had to use supercomputing resources to create all the metadata used to train the software, but the Autotune software that will be available to the public doesn’t require all these high-performance resources,” Sanyal said. “We commonly run the software on a laptop.”