Figure 3: Energy conservation through big data aggregation and analytics
Adapted from: “Monitoring-Based Commissioning: An Update” by Karl Brown, Deputy Director, California Institute for Energy and Environment, University of California, June 2011.
When you are able to collect, standardize and integrate data about buildings, you also create opportunities for significant improvement in energy conservation with the related financial savings. The core value application is the near real-time identification of equipment and system faults that waste energy. This identification enables you to take corrective repair action immediately, rather than finding the anomaly months or years later on the next calendar-based maintenance cycle. An all too common example of this involves the major component of a building’s HVAC system. An air handling unit (AHU) with a leaking steam valve will compensate for the added heat by ramping up the AHU cooling elements to deliver a desired air temperature. The AHU is spending more of your energy money to correct the problem while it wears out faster. The problem is likely to persist until the next scheduled maintenance, which could be in months or years, if at all. Event correlation analytics identify the fault of simultaneous heating and cooling and the root cause—the leaking steam valve—it occurs so you can fix the problem immediately, stopping the waste and unit degradation. For example, a Lawrence Berkeley National Laboratory study, commissioned by the U.S. General Services Administration and the U.S. Department of Energy, shows this approach is estimated to reduce facility costs for energy and operations by $0.52 per gross square foot per year.4
Figure 3: Energy conservation through big data aggregation and analytics
Adapted from: “Monitoring-Based Commissioning: An Update” by Karl Brown, Deputy Director, California Institute for Energy and Environment, University of California, June 2011.
The fault-fix cycle is an especially valuable aspect of analytics applied to maintain the savings obtained through traditional energy conservation measures (ECM) of physical equipment and system replacements, as well as periodic system retro-commissioning. Like all machines, the replacement equipment will begin to drift from optimum performance almost immediately. The application of analytics for an accelerated fault-fix cycle helps extend the duration of optimal equipment performance, identifies trends for predictive actions, and reveals new insights into system performance and energy consumption.
Beyond simply identifying the operating faults of equipment, aggregating hundreds of the fault-fix cycles allows you to make trending comparisons of repair records of units across many buildings to reveal which manufacturer produces the most reliable and efficient AHUs. Similarly, more closely monitoring outside contractors employed for each building is likely to reveal a wide disparity in costs and performance and indicate areas for improved actions and best practices to maintain efficiently. In addition, an often overlooked approach to energy conservation is rightsizing the space portfolio. Eliminating inefficiently used space by informing planning decisions with insights based on data from occupancy sensors can reduce energy and maintenance expenses. When data is used effectively, you can more easily develop quantified best practices and allocate resource efficiently across the enterprise.
Most industries have determined ways to optimize business information and human resources data across boundaries for central management. It is now possible, and necessary, to do the same thing with facilities management data that comes from buildings. Ideally, there should be a building portfolio command center where you can see exactly what’s going on across the enterprise in real time. With information about the core metrics for all of the systems and spaces, you can understand what has happened across your entire building portfolio, what might happen and what to do. First, you need the ability to sense, monitor and capture information from facilities and buildings data sources, especially for energy and space assets. It is also important to store and access that data using a data store or warehouse. The ability to analyze the data and identify potential areas of savings around energy, operations and space is key to improvement in these areas. Then, based on historical trending, current conditions and business rules, prescriptive corrective actions can be applied to ensure that a prepared and efficient service effort is used to restore optimal system conditions and align space with demand. Lastly, dashboards at the enterprise and operational level are needed to provide visibility into what’s been done, and the measurable cost avoidance achieved.
By collecting operational status data from disparate individual buildings and equipment, then aggregating and correlating the information in a singular view, followed by using advanced analytics to assess system performance, you can diagnose root causes of operational faults and recommend corrective actions. By enabling a transition from a reactive approach to a proactive one, predictive analytics can help create a more efficient, more desirable environment for owners, operators and occupants.
When an organization has the right data in the right hands, it enters a new era of management and operational control. Here are examples of how the move to data-centric smarter buildings brings benefits to individuals and organizations that own, operate, build and use buildings:
In an ideal situation, you would have an integrated energy management, work order and asset management solution that tracks, reports and analyzes energy and space use to enable an informed response that also measures monetized cost recovery from efficiency improvements. Integrated analytics can help you identify patterns across the portfolio so you can proactively address potential performance issues to minimize risks and maximize facility utilization. You need an enterprise solution that can aggregate, normalize and optimize data across an entire portfolio of buildings and building data sources. By taking advantage of a cloud infrastructure, it is possible for even geographically dispersed holdings to be viewed cohesively, and for dispersed facilities teams and separate departments to act cohesively as a portfolio team.
With this type of solution, building owners, managers and chief executives can more easily see the interrelationships among devices, energy and space, and recognize new patterns of facility use across boundaries. They can integrate isolated islands of information to extract new actionable insights to maximize their returns from the property portfolio.