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What’s All the Hype about Big Data and Energy Analytics?
There's an adage that is very true for energy: You can't manage what you can't measure. Analytics automates that process. For years, energy managers conducted this labor-intensive task manually and it was easy to put off. However, the principles are simple: measure and record baseline energy use, identify the high users first, take action, then measure again to verify progress.
Analytics software provides real-time information, just as many cars can report miles-per-gallon consumption in real time.
How does energy analytics work?
Energy analytics uses sophisticated software engines to track energy consumption. The simplest source of energy data is a building's utility meter, the ultimate cash register for energy. Older buildings may require meter upgrades to add a data interface, but with the advent of utility smart meters, these interfaces have gotten easier to implement.
Most building automation systems (BAS) already pick up meters as data points and can export consumption data in standard formats, e.g., via BACnet to an analytics application. For buildings without a BAS, a router or data logger can access data and communicate to systems over the Internet or building networks.
How do I get started?
There's another adage: Garbage in, garbage out. Visualizing energy performance is a powerful tool but for sound analysis it is critical to start with quality information.
The first step is developing an analytics architecture that accurately measures building performance. Sound complicated? Well, it really isn't. Much of this data comes from systems that are already in the building. Consider what data you need, including 1) building energy benchmarks for electricity and natural gas; 2) other energy units such as steam or chilled water in campus situations; 3) measurements for air quality, indoor temperatures, or equipment performance; and 4) electric rate analysis and/or demand response performance.
What if I have many buildings across the country?
For large organizations with nationwide portfolios, energy consumption by building is not a good metric. More valuable measurements might be energy cost per square foot, units of energy (kWh, CCF, etc.) per square foot, or BTUs per square foot. To normalize the data over regions with differing weather, the metrics often include degree day or bin hour measurements (monthly number of hours of weather experience at a given temperature for a geographic location). Most analytics tools include a weather database to compute this data automatically.
What if there is no utility meter on the building?
The meter interface is simplest, but many campuses have just one master meter for multiple buildings. In this case, analytics architectures must include submeters with network communications (wireless or cellular modems) for each building.
While it adds cost, communication is necessary to analyze data from the buildings and compare it to master meter bills. Addressable switchgear can provide a submeter on every major circuit breaker, enabling downstream analysis. This granularity is very useful for energy analytics and it is inexpensive for new construction.
Can data from any meter or BAS be input to any analytics system?
Data standards for analytics are a hot topic. Several IT standards are available, but they typically require extra steps due to multiple data formats. Project Haystack (haystackconnect.org) is an industry group working to address this issue. Another group is ASHRAE Standard Project Committee 201, Facility Smart Grid Information Model (spc201.ashraepcs.org).
What's the difference between energy analytics and building analytics?
Jim Lee, president of Cimetrics and one of the thought leaders in this space, says, "Energy analytics provides insight into one of the larger variable costs of operating a building today. But energy is only one component of operating cost. Building analytics offers a more holistic approach. It collects data from BAS, weather, meters, and scheduling and reservation systems to provide an enterprise management view of facility operations. Automatic fault detection and diagnostic algorithms mine the data and produce actionable recommendations to improve operations. Building analytics includes predictive maintenance, operational and labor savings, reliability, and compliance reporting."
What's the payoff?
Analytics can help ensure that capital investments are targeted at the projects that will save the most money. For example, comparing building baselines reveals the major consumers. Savings may be available simply by reprogramming BAS setpoints or investigating whether minor operational changes can reduce equipment use.
More importantly, without such tools the only way to manage energy is through the rearview mirror. The bill does not come until after the fact. Someone should be responsible for interacting daily with analytics and to create energy action lists.
Jack McGowan, CEM, DGCP, is president of Energy Control Inc. (ECI), an Optera Energy Company, and chairman emeritus of the DOE's GridWise Architecture Council. He has written five books and hundreds of articles. He was admitted to the AEE's International Energy Managers Hall of Fame in 2003.