What’s and expected Return on Investment from Building Analytics or a Fault Detection and Diagnostics (FDD) Deployment?
For those of you who are new to the industry, here’s a quick summary of what exactly I mean by building analytics or FDD. First off, consider the two terms, buildings analytics and FDD, to essentially be the same, so I’m going to stick with building analytics going forward.
Modern commercial buildings contain a number of complex systems, things like lighting, heating, ventilation and air conditioning (HVAC), security, vertical transport (elevators), and fire alarm. And this is just for your typical office building. Hospitals, laboratories, manufacturing, and industrial facilities will have even more systems than that. Each of these systems has the capability to create an enormous amount of data describing how the building is running. If used, or even stored, this data is very valuable for many of us in the building industry. Unfortunately, in most cases, the data is not created, meaning it’s not captured, recorded, stored and analyzed.
This is where building analytics come in. Building analytics have at least 3 components: data connection, analytics engine, visualization layer. The data connection, or data pipe as I like to call it, is really two pieces. A connection to the building from the outside world that navigates through the building’s firewall. The second piece is the connector that connects the building analytics database to the building system. Once the data pipe is connected and data is flowing, we can start driving value from the information.
This happens in the analytics engine. The analytics engine includes a database that stores time series data that describe how a building and its components are running through the day, week, month and year (s). Just having access to this data is valuable, but the analytics engine takes it step further by continuously applying a set of rules to the data, looking for problems with system operation or opportunities to improve performance.
The analytics engine is going to do a very thorough job of data analysis and will provide a long list of items that someone is going to need to address. This is where the visualization layer comes in. The visualization layer takes the results from the analytics engine and brings it forward in a way that helps engineers, energy managers, facility managers, directors, and the C-suite take advantage of the data. This will usually include key performance indicators, tailored to specific user needs as well as prioritization tools to help end users find what’s important to them. From here, the end user takes action and drives the ROI.
ROI from a building analytics deployment comes from three major areas: Energy, Comfort, and Maintenance.
Energy efficiency is the first place most people look to drive value from building analytics and it makes sense. Energy efficiency improvement are quantifiable, many times by the analytics engine itself, and the savings can be verified easier than ever before though utility bill automation or direct measurement of meters or key data points.
A typical energy efficiency ROI from a building analytics deployment ranges from immediate to 3 years. Here are a few examples of what is typically uncovered:
- Scheduling: Its always amazing how often a building’s HVAC schedule is not adjusted or maintained to match occupancy. Mainly because it’s really “invisible” and can be easily forgotten about. Scheduling does take some up-front work to really figure out the operating schedules in a building, but once you have that upfront work done, building analytics can really help maintain that schedule. This is a great place to start and a real low cost opportunity to save some energy and money.
- Setpoint adjustments: Less obvious than scheduling, usually for this you’ll need an experienced consultant to advise you on how to adjust the setpoints so that savings persist and you don’t end up with unhappy tenants and reverting back. These setpoint range from space temperature setpoints – making sure that there’s enough “space” between heating and cooling setpoints, reducing airflow at night when buildings are not occupied, to air handling unit temperature setpoints and reset strategies (how buildings change setpoints to react to different indoor and outdoor conditions), to specific control setpoints in in your chilled water and hot water plants. Often, we find key set points at the plant level overridden to band aid issues at the space level, overrides get forgotten and end up causing huge inefficiencies throughout the building. Buildings analytics can both help you identify where there are opportunities to adjust setpoints, and after you do analytics will help you maintain the setpoint adjustments.
- Minimizing simultaneous heating and cooling: As counterintuitive as it may seem, most buildings are designed to take warm air (for example from your office area), filter it, cool it down, and then heat it back up. This is the phenomena known as simultaneous heating and cooling. There are a number of reasons why this occurs and even more strategies to minimize it. Building analytics will help you identify when its occurring but to really solve the issue you’ll likely need to adjust the control strategies in your building which will involve consultants and control contractors. The good news is the savings are typically significant.
- Control sequence optimization: I hit on one target above, minimizing simultaneous heating and cooling, but there’s more opportunities out there than just that. In fact there’s a good chance that from the thermostat to the central plant, there will be new control strategies that will allow your building to more efficiently keeps its occupants happy. Building analytics constantly reviews how your building is operating and will flag when sequences are not optimized and there’s an opportunity for improvement, but in this scenario just having access to data is enough for an experienced operator or consultant to identify optimization opportunities.
- Minor capital projects: These projects vary greatly, from installing occupancy or CO2 sensors and taking your scheduling work to the next level and implementing a sensor-based scheduling program. From experience its amazing to see how underutilized most buildings really are when they are “occupied”. The other end of the spectrum could be something like installing the hardware required to install deep chilled water plants analytics in your central plant allowing you to really understand, optimize, and maintain (we’ll get to that later) one of the largest energy users in a building.
There is a common theme in all of the above points. Data and building analytics will help identify opportunities and maintain the savings, but they will not drive the value for you. You will need experienced and engaged facility management staff, who embrace technology and the value it can bring, and consultants that are used to working with data/buildings analytics to identify issues, determine the root cause analysis and implement the solutions.