October 2011

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The Next Generation of Building Energy Efficiency

Given a building’s energy model and external factors, the software determines the best plan for the building, targeting the ideal balance among the three goals of energy cost, consumption, and occupant comfort.

Mike Zimmerman
Mike Zimmerman,


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Commercial buildings in the United States represent 20 percent of our annual energy consumption. With rising energy costs and an increased focus on energy performance, building owners are looking to save energy and reduce their utility expenses with less capital and resources. This translates to building materials and systems that must be far more energy efficient and cost no more, if not less, than conventional systems.

With commercial buildings, satisfied occupants set the bar for success. However, this conflicts with the expectation of solid financial performance. The challenge facing building owners is to manage these conflicting goals. A third factor in the mix is the need, either voluntary or regulatory, to reduce emissions.

Proactively reaching these three competing goals in a complex commercial building environment becomes almost impossible with today’s building management system (BMS) tools. Despite high levels of control at the sub-system level (i.e., water and air temperatures and flows), there is limited global control and direction across the entire building. What’s more, there is not awareness within the BMS of other factors, such as weather forecasts or utility rates, which greatly affect costs and emissions.

These challenges are tough, especially given the complexity of most buildings and their energy systems. With the downturn in the industry, there are fewer managers in buildings. In fact, the best building managers are being pulled up a level to manage a portfolio of buildings, leaving less expensive and less skilled managers on the ground to make the difficult day-to-day decisions.

Redefining Building Energy Management
In the face of these challenges, new technology is emerging to address the problems with a promise to redefine the way energy is managed in buildings. The technology helps owners reduce energy costs without affecting tenant satisfaction. It also helps building owners and managers proactively manage energy, while freeing up their time to focus on other important tasks. Best of all, the technology is delivered as software, resulting in lower costs and shorter payback cycles. One example of this new technology is Predictive Energy OptimizationTM software from BuildingIQ.

In a nutshell, the software understands the internal and external conditions of a building and guides the energy systems to achieve its goals. It overlays the BMS and optimizes energy use by continuously changing BMS set points.

In terms of internal conditions, the software considers many aspects of the building:

•    Thermal characteristics
•    Mechanical-plant operations and capacity
•    Existing BMS settings
•    Tenant occupancy times and load variation.

The software learns the internal operations and creates a complex energy model that enables accurate forecasting of building energy requirements under different conditions. It also considers external conditions:

•    Weather and weather forecasts
•    Building energy prices and tariff structure
•    Electric utility-grid information and power delivery occurrences, such as Demand Response (DR) events.

Given a building’s energy model and external factors, the software determines the best plan for the building, targeting the ideal balance among the three goals of energy cost, consumption, and occupant comfort. The plan is set a day ahead and is continuously re-optimized based on any changes to internal and external conditions.

Figure 1 illustrates how Predictive Energy OptimizationTM works on the HVAC systems in a typical building during a typical day. Note that this represents data from an existing building in which Predictive Energy OptimizationTM software has been deployed. The blue line shows the power levels from the existing settings on the BMS, starting every day at the same time to ensure the right conditions at occupancy, running approximately at the same power levels all day to maintain the target temperature, and shutting off at the end of the day.
Predictive Energy Optimization
Figure 1: The settings on the BMS start every day at the same time to ensure the right conditions at occupancy, then run at about the same power levels all day and shut off at the end of the day.

In contrast, the red line shows the power levels that result from energy optimization software, which has been tailored to the specific day’s weather and the utility rates. The morning start-up is set to take advantage of low overnight temperatures and the building’s thermal mass to store energy while achieving the right conditions at occupancy with much less power than the standard settings.

Power levels are increased as the day warms, and later in the afternoon the system ramps up power to cool the building before the peak electricity-rate period. This same approach is used to automate the response to DR events, decreasing loads when the electric grid is strained. After the rate changeover, the earlier cooling allows the building to “drift” on much lower power levels, with gradual changes to maintain comfortable conditions throughout the rest of the day.

New Solutions to Old Problems
Predictive Energy OptimizationTM software can be used with a wide range of BMS types and HVAC infrastructure. It requires no upgrades to a building or new sensors, and only needs some minor sub-metering and an Internet connection. Installation and system tuning are heavily automated, with most of the configuration work done remotely after the initial installation.

The software can be specified for both new construction and retrofit projects. It makes sense to install a system such as this at the time of a BMS upgrade to coordinate any programming work that is needed. However, that is not a significant consideration. Many of the new software products bring software business models to the building industry, charging subscription fees rather than upfront costs. The subscription fee for Predictive Energy OptimizationTM software is based on the size of the building, with no capital outlay to install and get started. As such, the system should pay for itself within the first month or two of deployment.

Building energy savings of 10 to 30 percent on a continuous basis are not uncommon. This level of improvement, which could translate into an increase in Energy Star points, could mean the difference between getting and not getting an Energy Star label or LEED certification. Even greater financial benefits may result, given the significant reduction in peak-tariff energy use. In addition to the financial benefit, the system dramatically reduces the time required to manage energy in a building, thereby freeing up the building engineer to focus on other tasks.

Overall, while the circumstances of installing this kind of software are not particularly significant, the long-term savings in energy and labor costs can be huge. Technology of this kind is truly providing the next generation of energy efficiency in the commercial building space.


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