Advanced tech saves agencies energy, costs

How the US will build a power-hungry future with less energy

The federal government uses a lot of energy.

According to the Energy Department’s Comprehensive Annual Energy Data and Sustainability Performance annual report, in FY 2013, federal agencies reported spending $6 billion in energy costs in buildings subject to energy reduction requirements. Using technology to trim even a fraction of a percentage in costs can add up to huge savings.

The General Services Administration just released a request for information on innovative building technologies as part of its Green Proving Ground program, which leverages the agency’s real estate to evaluate energy saving technologies.

So far the agency has adopted technologies such as wireless network sensors that provide real-time information enabling facility operators to better manage heating and cooling systems. And advanced power strips with schedule controls have resulted in a 26 percent energy reduction at workstations with advanced computer management already in place.

Other agencies are likewise working to cut energy use in buildings, including the National Institute of Standards and Technology, which is testing the use of intelligent agent technologies.

“Adapting intelligent agent technologies from other fields offers the promise of significant improvements in building operations,” said Amanda Pertzborn, a mechanical engineer working in NIST’s Embedded Intelligence in Buildings Program in the agency’s announcement.

“The idea is a kind of ‘one for all approach’– use networked intelligent agents to manage and control devices and equipment subsystems to enhance the overall performance of a building rather than to optimize the operation of each component independently of all the others.”

Intelligent agents are combinations of software and hardware such as sensors, mechanical devices and other artificial intelligence tools.  NIST is converting one of its laboratories to a 1,000 square foot mock office building to test whether the tools can be collectively used to optimize building performance, including minimizing energy use, enhancing employee comfort and improving security.

These artificial intelligence tools can perceive their environment, make decisions, take actions as well as learn, predict and adapt to changing situations.

According to the DOE’s Buildings Energy Data Book, in the United States, the buildings sector accounted for about 41 percent of primary energy consumption in 2010. Furthermore, total building primary energy consumption in 2009 was about 48 percent higher than consumption in 1980, with space heating, space cooling and lighting accounting for nearly half of all energy consumed.

The lab will initially test how to improve the most energy-intensive of building operations: heating, ventilating and air conditioning. HVAC systems in commercial buildings account for about 7 percent of total U.S. energy consumption, according to NIST.

When initially installed and tested, an HVAC system “starts out performing at peak efficiency. Over time, however, efficiency tends to degrade … requiring retesting and retuning the system,” NIST said. “Intelligent agents distributed throughout a HVAC system would enable continuous tweaking to orchestrate the operation of all components so as to maintain peak performance and efficiency throughout the building’s lifetime.”

The testing mirrors work done by the Lawrence Berkeley National Laboratory, which is also working on improving building efficiencies. Originally proposed in a 2008 paper by lab researchers Bruce Nordman and Alan Meier, the idea is called "implicit occupancy sensing," and it posits that using existing infrastructure for occupancy sensing could reduce energy usage. 

Noting calls made on telephone landlines, communications data collected by routers and Wi-Fi access points to measure the presence of smartphones, computers, and security badging systems could determine not just occupancy numbers but which floors and rooms are in use, noted a Berkeley Lab article on the idea.

With this knowledge, lighting, heating and cooling could be more finely tuned to the actual usage of the building.

“If a building could base its operations on people actually showing up rather than a fixed schedule, the potential for energy savings could be quite large,” Nordman said. “And if you use existing devices to sense occupancy rather than wiring a dedicated sensor and maintaining it, then it’s free – you don’t have to spend money to save energy.”

In April, Berkeley Lab and Webcor announced the kickoff of the Flexlab experiment, a building efficiency test site.

“By measuring a building’s energy use under real-world conditions and on a significant scale, Flexlab provides real time assessment of building energy use,” noted the program’s brochure. “Stakeholders can evaluate energy-efficient building technologies individually or as integrated systems in advance of building projects or retrofits.”

Similarly, the National Renewable Energy Laboratory, part of the Energy Department, is working on a new, efficient data center, the Energy Systems Integration Facility. The super-efficient data center, in Golden, Colo., is designed for researching how to best use renewable energy. The facility will carry out research, development and megawatt-scale testing to improve the efficiency of electricity, with every bit of energy at the facility being reused in some way. The $135 million facility will be 185,000 square feet when complete.

There are stumbling blocks to overcome in all of these initiatives, namely different methods of measurement and language when discussing and measuring energy performance, said Dr. William Miller of Berkeley Laboratory in a Q & A article on the lab’s site.

Miller is working on developing a “dictionary” of terminology and definitions – the Building Energy Data Exchange Specification (BEDES), to define a common data format, definitions and exchange protocol for building characteristics, efficiency measures and energy use, he said.

“This is helpful on so many levels. For one example, states like to compare their performance to other states. So, is California doing better or worse than Massachusetts? If they don't agree on the data input, there is no way to know,” he said.

Miller is also working on the Uniform Methods Project, a measurement standardization project. “If you look at how different states measure energy efficiency savings in utility programs across the country, results differ dramatically. This is because states use different methods of measurement,” he said.

“Our thought was that if we use essentially the same calculation process, we could break down what caused the difference. We could see where the difference came from.”

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