Predictive Energy Optimization

Predictive Energy Optimization

What is Predictive Energy Optimization? 1 of 5 Predictive Energy Optimization (PEO) is the trademarked term for BuildingIQ s software platform designed to improve the energy-efficient operations of large, complex buildings, whether commercial, public, or academic. Running as a software-as-service (SaaS), PEO optimizes around system efficiency, occupancy comfort and lowest cost. Energy reductions in the range of 10-25% are typical, with reductions climbing to as high as 40% during operational peaks. First step learning PEO begins with physics a set of generic rules that govern the heat transfer within all buildings. This forms the core of the model. Then the model s algorithms set about the task of learning the specific dynamic responses of the building to a wide variety of continuously changing conditions everything from temperature and humidity, to occupancy profiles. Measurable data inform hundreds of parameters that monitor and ultimately control the building s dynamic responses. Day 1 of the learning process begins by comparing actual data to the model s first 24-hour simulation. Early parameter fittings can be miles apart from reality as more data becomes available. The model goes to school. With more information, more comparisons, and what-if combinations, the model tries to bring the model simulations and the actual readings closer together day by day, even as the weather around the building changes. It s a little smarter on the second day, possibly surprised on the third day, a little smarter still on the fourth day, and so on. Second step prediction The BuildingIQ learning process, which is designed to work in concert with the existing building management system (BMS), is one of gradual convergence between parameter based simulations and actual readings. It takes some 4-6 weeks for the model to truly understand the unique dynamics of the building thermodynamically, as people enter and leave, congregate and disperse in various zones, as weather conditions change, and as the train of heating and cooling equipment labors with partial load or hums along at peak efficiency. Accurate prediction of the building s thermal behavior, in aggregate and zone by zone, under changing conditions is the precursor to optimization.

Building IQ in Action Pre-cooling and Demand Response 400 100 90 Zone Temperature Power (KW) 300 200 80 70 60 50 Temperature ( F) Ambient Temperature Baseline Power BIQ Optimized Power 100 40 Work Hours 30 0 3AM 6AM 9AM 12PM 3PM 6PM 20 Optimized Start Time of Day High Peak/Low Demand Charge Tariff Third step: optimization Tenant comfort is one of the key priorities, but cannot be controlled as an absolute constant, and should not eclipse all other priorities. Multi-functional decision-making is part of the design of PEO. Temperature can and does vary in a given space by one, two, perhaps even three degrees without noticeable discomfort. This variance, coupled with the ability to predict the building s dynamic response to various contingencies, opens the door to BuildingIQ s optimization of comfort, cost, and efficiency. Knowledge of power prices in the marketplace, as they rise and fall during the day and by season, as well as the economic benefits for participating in demand-response (DR) programs are factored into the decision making process of the model. The result is the optimum power profile to yield the lowest cost, highest level of HVAC system efficiency, and maximum comfort. The building might be cooled several degrees below midlevel comfort during the morning in order to let the temperature drift upward, while backing off on power demand during peak pricing on a hot afternoon. Page 3

How Fast Can the System Learn? 2 of 5 The majority of building systems are not active learning systems; rather, they tend to be preconfigured in the way they respond. They have limited ability to adapt to a novel situation beyond the hard-coded if, then, else type of instructions made available to the control system engineer. In contrast, BuildingIQ s Predictive Energy Optimization (PEO) model begins learning the dynamics of the building from the day of implementation and continues on for the life of the system. On day one, the system has a set of generic rules but no data, and the predictions of the power profile required to meet the building s load can be wildly off the mark. Nevertheless, these misses are important bits of information for the model. Although there are hundreds of parameters and billions of possible combinations of these parameters, the system begins to search judiciously, sampling different combinations of the parameters that can possibly match the model simulation to the actual data. There may be thousands of combinations that could give a decent fit to yesterday s data, but meanwhile weather conditions have changed. The building s response to varying conditions hotter days, colder nights, more humidity, less humidity offer further clues to the many dynamics observable within the building. The model factors in this growing body of data, trying to narrow the gap between prediction and reality. The winnowing process tries to understand which parameters are most relevant under all the conditions available since data collection began. The learning process is continuous. Each day, the system becomes a bit smarter, honing in on the parameters, and comprehending ever more subtle variations in building dynamics. The defining parameters, which initially jumped around erratically, finally begin to settle. Slowly, over a matter of weeks, the predictions converge with the real data coming from the building without the previously observed parameter movement. This is the critical juncture in the model fitting process. This settling of model parameters indicates that there is a consistency that has been achieved and the system is now reflecting reality. Said another way, the fabric of the building and the performance characteristics of the plant do not change day by day and, therefore, neither should the model parameters. BuildingIQ has found the initial learning process takes about 4-6 weeks after implementation. At that point, they are confident the PEO model can accurately predict the power profile for the building for the next 24 hours. The 24-hour window of prediction is automatically updated every four hours, so the system is always responding to this most current and relevant target. Data convergence is then taken to the next level: optimization. After the 4-6 week transition period, the model is able to identify and activate the pathway to the lowest possible operating cost for the building to modulate comfort within the constraints established by the building operators. Optimization factors in peak pricing, building and equipment efficiency measures, and programmatic variables, such as demand response.

During the 4-6 week transition period, HVAC controls slowly incorporate the PEO system. In the beginning, the legacy BMS system remains in control. After the second week, PEO controls are gradually, incrementally introduced, only where needed and in such a way that tenants are not inconvenienced. BuildingIQ s philosophy involves a light touch, meaning selective control. It does not replicate things the current BMS does well. On a typical BMS, there may be many hundreds or even thousands of points and BuildingIQ s system will only interface with approximately 5-10% of those points. Pre-programming a building for HVAC controls is no longer a fine enough instrument for advanced building management. Adaptive learning and rapid, multi-functional response of the kind delivered by PEO is needed for peak performance and cost control. Page 5

My Building is Different 3 of 5 The belief that every building is different completely, utterly unique can be taken too far. Yes, when considering all the particulars of design, material composition, facing, glazing, mass, substructure, utilities, and the like, one can argue no two buildings are identical, just as one could argue that no two people are exactly alike in all particulars. But the general physiology of humans blood circulation, digestion, etc. is something we share. The same can be said for buildings. The truth is that the laws of physics apply equally to all buildings, thus establishing a solid base upon which to build an energy management system. The physics lead to a fixed set of rules that underpin generic solutions to the dynamics of heat transfer into and out of the building, and between floors and among the many interior zones as people move about and occupy space. Going beyond the fixed laws of thermodynamics, BuildingIQ also incorporates a range of additional dynamics encountered within buildings. These dynamics include part load efficiency of plant, occupant loads and a range of other items, which impact both internal building temperatures and the corresponding power load. The key is to accept the various rules and finetune the descriptive parameters. For software, this means starting with a generic description of the building, then adjusting the fittings and settings through customizable, adjustable parameters. Adjustment comes from listening to the building, taking in the data the building throws off in the course of daily operations, and learning from it. In the case of BuildingIQ s Predictive Energy Optimization (PEO) system, the data allows the model to learn and eventually understand the building s behavioral dynamics the rate at which outside temperature and interior temperature converge when the HVAC system is turned off, for example. Through the learning process, the predictions that are generated by the model become closer and closer to matching reality. At that point, when the building is understood, both generically and specifically, then the optimization process can begin to generate meaningful results. With buildings, the desire of engineers to see their building as completely unique, is understandable, but can lead to the pursuit of ultimate flexibility and ultimate customization when designing the energy management system. There is a temptation to build a physical model of the building to learn the dynamics, rather than listening and learning from the actual building itself. BuildingIQ s platform takes the latter course, listening, learning, and adjusting the model based upon the data coming from the performance of the building.

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What Does the Model Capture? 4 of 5 The Predictive Energy Optimization (PEO) system captures the most important dynamics of a building s thermal and associated energy consumption behavior. Each dynamic, which is then coded into the model s software, contains anywhere from one to five parameters that jointly describe the range of motion of this dynamic, and its interactions. A sample of five dynamics captured in the PEO system are described below to illustrate this: Thermodynamic Parameters The speed of convergence between outside temperature and inside temperature when an HVAC system is turned off provides much insight into the performance of a building s fabric. The key is the rate at which these temperatures converge asymptotically to ambient conditions. Heat migrates much faster through a western-facing glass wall, for example, than through a concrete bunker. PEO uses an equation that describes the speed at which these two temperatures converge when artificial temperature control is removed. Interestingly, the model fitting process used by BuildingIQ does not need to specifically observe the time periods immediately following HVAC shutdown. The signature of the building fabric performance is, of course, present in all data. Occupancy Parameters Every occupant that comes into a building introduces an additional heat source equivalent to a 60-watt lamp. Office workers typically arrive between 7:00-9:00am, move in and out during lunchtime, and leave for the day between 4:00-7:00pm. Using generic occupancy profiles, and once again looking signatures hidden in the data, the heat load from employees can be parameterized to create a day of week and time of day specific load. Looked at another way, the model fitting process is essentially looking for periods where there are heat loads presented to the plant that cannot be explained by the thermal interaction with ambient weather. Unlike the impacts of weather, the occupant patterns provide a range of loads which although they change by the hour and day do not change based on ambient conditions. This thermal load can then provide the best prediction of power requirements to meet the human based heat contribution when combined with the parameters that describe the performance of the underlying HVAC system. Plant Derating Parameters The performance of a cooling plant is significantly affected by outside temperature and humidity. In hot, humid weather, the efficiency of the cooling plant deteriorates, increasing the effective plant load required for cooling the building. The model captures this relationship in a generic way and factors it into the prediction of a building s overall power requirements.

Much like the thermo-dynamics captured by other parameters, the derating parameters look to describe the link between the total, internal loads and the subsequent power levels observed in the data. Without a derating parameter, the error between total predicted power and actual power was found to vary further in a way that went beyond those dynamics captured in the building fabric parameters. As well as decreasing the errors in the model, the additional awareness provided by derating parameters allows for the exploration of additional optimization opportunities. For example, this allows BuildingIQ to calculate the possible benefit made available by running cooling plant at higher loads during a cool morning in preparation for a hot afternoon. Free Cooling Parameters When weather changes and temperatures drop, opportunities for free cooling arise. If outside temperatures, for example, are in the 50-60 F range, natural venting allows the buildup of interior heat to be carried to the outside. Free cooling as a building dynamic is captured in the PEO model. The model has incorporated several parameters around the generic implementation of free cooling that describe not just the change in the effective performance of the building fabric but also the control strategy used to implement free cooling. This is an interesting example demonstrating that even though BuildingIQ is not in direct control of the free cooling strategy, the model has awareness of when free cooling is very likely to happen. When this intelligence is made available to the optimization system then macro level strategies can be devised that extend the opportunity made available by free cooling. On the one hand, the optimization may drop the space temperature as far as possible during free cooling effectively storing more cooling for later in the day when cooling with come at a significant cost. On the other had, the Optimizer may reduce the effect of free cooling by raising the space temperature when it knows that a cold afternoon is approaching and heating will be required if too much cooling is put into the space right now. Fitting Algorithm The PEO model captures and fits roughly 40-50 parameters for the individual zones of a large commercial building, as well as a wide range of parameters at the whole building level. The four examples described here thermodynamic, occupancy, derating, and free cooling are more applicable at the whole building level. BuildingIQ describes the process of establishing the right parameters and capturing the data as it comes from the building as one where the physics meets the math. The alternative, dubbed as physics meets engineering, describes the process where engineers build a computergenerated physical model that they use for design of the HVAC system. BuildingIQ believes listening to the actual building rather than a simulation of the building moves advanced HVAC system automation much closer to reality much faster, and at lower cost. Page 9

Myth Busted: My Building Runs to a Fixed Set 5 of 5 Some building managers like to insist their building runs to a fixed set point, for example, 72 degrees flat out, with no variation. The HVAC system simply has to adapt to the reality that tenant comfort is not only preeminent but also absolute. Their underlying assumption, however, is that interior temperatures can be controlled so precisely, and this assumption is not born out by experience or measurement. Careful monitoring of thousands of buildings shows there is significant variation in temperature throughout the day, and from zone to zone, despite the best efforts to exact rigid control. Engineers refer to a dead band that exists around established set points of plus or minus 1.5-2.0 degrees on average, with it sometimes reaching 3 degrees. This implies a potential swing of 3-6 degrees, which has traditionally helped avoid HVAC systems overreacting. Using a fixed set point as command, an HVAC system may call for 100% heating one minute followed by 100% cooling the next. The inefficiencies associated with rigid setpoint control compound throughout the day. Variability as a Strategic Asset A more enlightened, energy-efficient perspective would simply accept the reality that interior temperatures vary, and acknowledge that efforts to control them to a fixed set point are futile. Acceptance of inherent variability allows system designers to put the variability to good use. Strategically, the thinking goes, if the building varies, why not make it vary at the smartest times of the day? On a very hot day, for example, the Predictive Energy Optimization (PEO) model can drive the building down to a temperature of 70 degrees in the morning hours, before the heat of the afternoon, then let the building coast over the 2:00-3:00pm period when temperatures outside are the highest, power costs are peaking, and cooling-plant efficiency is at its lowest. In principle, the building can be directed to coast upward gradually to 74 degrees by say, 6:00pm. That activity alone might remove as much as 50% of the load from the plant. Modulating Comfort The key is not to fight variability but to use it strategically. Taking this to its logical conclusion, HVAC designers might be willing to commit heresy by saying, Let s modulate comfort as a degree of freedom in the system. Comfort has been something that everyone in the HVAC industry has looked at historically as a fixed, almost sacred commodity. Yet knowledge that temperature in a building is in a constant state of flux within an acceptable band of comfort (e.g. 70-74 degrees) opens up the client side of the HVAC supply-demand equation to new opportunities. Opportunities on the Demand Side Drawing an analogy with the power system, utilities have traditionally looked after supply in isolation, assuming the demand-side to be an alternative world beyond their control. That of course, has changed dramatically over the last 25 years, as demand-side management has come to the fore,

Point and as energy efficiency has become the option of choice for meeting load growth. Utilities in the Northwest region, for example, have set an ambitious target to meet 85% of its anticipated load growth over the next 20 years through conservation alone. Utilities are now talking about grid edge and transactive energy that capture the essence of partnership with the client. Modulating comfort doesn t mean driving the occupant to a state of discomfort; rather, it means using the established and acceptable sense of temperature variability within a building to optimize system efficiency and operating cost. Page 11

Predictive Energy Optimization About BuildingIQ BuildingIQ is a leading energy management software company with a mission to redefine and enhance the way energy is managed in commercial buildings. BuildingIQ s unique, patent-pending Predictive Energy Optimization technology is the foundation for reducing energy cost and consumption. It is designed to help building owners, managers and tenants get more value out of their existing energy systems. BuildingIQ has leveraged over 25 man-years of building controls, modeling and comfort research by world-leading experts at CSIRO, Australia s national labs, and BuildingIQ to create this innovative platform in energy intelligence. The company has been honored as Winner of the AIRAH Award for Excellence in Innovation, Tech23 s Greatest Potential Award, ED+C and Sustainable Facility s Readers Choice Award and Red Herring s Asia 100 Award. Predictive Energy Optimization The BuildingIQ system is the only energy management system that predicts energy demand and directly adjusts the HVAC system parameters in real time to optimize energy use. BuildingIQ communicates with your BMS factoring in weather forecasts, occupant comfort, peak demand, and demand response signals in order to automatically reduce energy consumption, cost, and emissions while maintaining or improving tenant comfort. 2014 BuildingIQ, Inc. All rights reserved 1065 East Hillsdale Blvd., Suite 310 Foster City, CA 94404 USA www.buildingiq.com biq-sales@buildingiq.com Predictive Energy Optimization 12/14