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Development of a whole building model predictive control strategy for a LEED silver community college
Energy and Buildings 111 (2016) 224–232
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Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Development of a whole building model predictive control strategy for a LEED silver community college Trent Hilliard a,∗ , Lukas Swan a , Miroslava Kavgic a , Zheng Qin b , Pawan Lingras c a b c
Department of Mechanical Engineering, Dalhousie University, PO BOX 15000, Halifax, NS, Canada B3H 4R2 Green Power Labs, 1 Research Drive, Dartmouth, NS, Canada, B2Y 4M9 Faculty of Computer Science, St. Mary’s University, 923 Robie Street, Halifax NS, Canada, B3H 3C3
a r t i c l e
i n f o
Article history: Received 14 August 2015 Received in revised form 18 November 2015 Accepted 19 November 2015 Available online 23 November 2015 Keywords: Model predictive control Thermal comfort Energy efficiency
a b s t r a c t A model predictive control strategy method is developed for a LEED silver community college building that contains an advanced common loop heat pump HVAC system. MPC is performed using persistence based climate forecasting with two scenarios examined: fixed temperature setpoint pairs; and a deadband of 1 ◦ C between the heating/cooling setpoint. EnergyPlus acts as the “real” building and is linked with R software for model predictive control via the Building Controls Virtual Test Bed. A statistical based building response model is created in R using training data from a calibrated EnergyPlus model. A brute force optimization strategy is used for solving the objective function. A piece-wise objective function maintains thermal comfort during occupied periods with a focus on reducing energy consumption during other periods. Results when using fixed setpoint pairs exhibit a 4% reduction in HVAC energy consumption, and deadband setpoints exhibit a 9% reduction in HVAC energy consumption, compared with reactive rule based control. Of interest is that the type of energy savings (thermal energy vs electricity) varies depending upon the setpoint options. The results are promising given the strict thermal comfort requirement employed, minimal search space, use of simplistic persistence forecasting, and the sophisticated HVAC system. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Commercial/institutional and residential buildings are the second largest consumer of energy in the Canada, accounting for 29% of all energy consumed nationwide [1]. Of the energy used in buildings, 55% is for space heating and 3% for space cooling, resulting in a total of 58% used for space conditioning. In Canada, space conditioning of these building types uses 1412 PJ of energy annually, and thus is a massive market where even small gains across the entire sector constitute large energy savings. Due to the vast amount of energy and cost associated with buildings, it is important to find methods to reduce energy consumption. While energy efficient retrofits are one path to achieve savings, they often have high capital costs, and take significant time/effort to implement [2]. A second approach is to modify how the existing equipment is being utilized in a building through the implementation of new control strategies. Within a building the controls can be broken down to two main categories: local controllers that
∗ Corresponding author. Tel.: +1 902 499 8574; fax: +1 902 423 6711. E-mail address: [email protected] (T. Hilliard). http://dx.doi.org/10.1016/j.enbuild.2015.11.051 0378-7788/© 2015 Elsevier B.V. All rights reserved.
operate a specific piece of equipment (such as fresh air dampers), and supervisory controls that supply target values that drive local controllers [3]. While many local controllers are finely tuned to provide reference value tracking from the manufacturer, supervisory controllers are often preprogrammed based on expected occupancy periods, internal loads, and envelope characteristics. Often times these parameters can change over time (such as a shift in building clientele), or are assumptions that do not account for issues during construction [4], [5]. Due to these factors, supervisory control at a building level is an ideal location for advancements in control logic that is adaptable to changing conditions. Model predictive control (MPC) is such a strategy. MPC is a branch of control theory that utilizes a model of a system or process that is subject to constraints, and attempts to find an optimal solution to an objective function based on current and forecast values. The optimal solution is typically found by solving an objective function (typically a cost minimization) [6], with constraints that limit the range of output control values. A prediction horizon is used, along with a specified timestep to allow the optimizer to look into the future and consider dynamic effects so that an optimal solution is found over the entire forecast, not just the current timestep [7]. The use of forecast information allows the
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education building servicing 2500 students. The building consists of 5 occupied floors, with a 5400 m2 footprint, for a total of 27,000 m2 of conditioned floor space, and is operated year round. Zone types include classrooms, open space, atriums, laboratories, workshops, cafeteria, offices, and mechanical rooms. The HVAC system for the building consists of four main components:
Nomenclature AHU BCVTB BRM C E E+ HP HVAC MPC r R VFD W
225
air handling unit building controls virtual test bed building response model cost of optimization error between prediction and desired reference EnergyPlus heat pump heating, ventilation, and air conditioning model predictive control reference values for optimization R statistical computing software variable frequency drive MPC weighting matrices
• Main water loop which links the steam converter (serviced by a district heat system) and cooling tower to the heat pumps servicing the air handling units (AHU) and zone level heat pumps. • AHU glycol loop which is controlled by water to water heat pumps on the main water loop and feeds the coils in the AHUs. • Main air loop which provides supply and return air to the zones, including the use of a heat wheel. • Zone level water to air heat pumps which transfer heat between the main water loop and the zone air.
system to adapt to changing climatic conditions, or changing internal dynamics, depending on how the problem is defined. Several review papers on MPC for buildings have been recently published [3,8,9]. Readers are encouraged to investigate these as they detail the evolution of MPC for buildings. In this paper a persistence based forecast MPC strategy is used to reduce HVAC energy consumption of a whole building. The persistence forecast consists of measuring the current value of a forecast variable, and assumes it does not change throughout the forecast horizon. Two different control strategies are used for the MPC: one with fixed setpoint pairs (e.g., heating and cooling values are the same) and one with a deadband of 1 ◦ C between the heating and cooling value. The results of the MPCs are compared to the existing rule based control (RBC) for the building outlined in Section 2. The paper shows that for a new LEED silver certified building, MPC does offer energy savings compared to the existing RBC strategy. The paper also introduces the use of R statistical software linked with EnergyPlus (E+) [27] for modeling and objective function calculations. Section 2 describes the building that has been modelled for the development of the MPC. Section 3 outlines the MPC method, starting with the software used, followed by the objective function, system timestep and forecast horizon. Section 4 outlines the work in developing a statistical black box model for use within the MPC, and a verification analysis is provided to validate the modelling methodology. Section 5 discusses the results of the 2 MPC scenarios in comparison to the existing RBC control strategy, highlighting the value provided by MPC. Section 6 concludes the paper with a discussion of the results, and suggestions to further improve the results of MPC in this building. 2. Building The Harbourside Wing of the Nova Scotia Community College Waterfront Campus, shown in Fig. 1, is a LEED silver certified [10]
Fig. 2 shows how these systems are interconnected. The main water loop feeds the zone level heat pumps and the AHU heat pumps which are connected to the air stream via the AHU glycol loop. The AHU glycol loop heats/cools the incoming fresh air to the building before it is delivered to the various zones. The zone level heat pumps provide the final conditioning to the zones, and will work to make up any deficits in heating/cooling from the main air loop. The main water loop allows for energy transfer between zones as the heat pumps push and pull energy from the loop. The loop is maintained between 28 ◦ C and 32 ◦ C to allow for optimal heat pump coefficient of performance, with [28] outlining thermodynamically the effects of inlet water temperature on heat pump coefficient of performance. The existing control strategy at the NSCC Harbourside Wing has four operating scenarios as outlined in Table 1. There are two strategies for each season (heating and cooling) based on building occupancy (described in Table 2). Each strategy has a unique palette of setpoints as outlined, while the type of control is listed under “Note”. A calibrated energy model of the building was created in E+ using as-built design drawings, air balance test results, operating strategies described above from the building operator, and the monthly energy billing data (thermal energy and electricity for 1 year). The model consists of 32 thermal zones and a detailed HVAC model as shown in Fig. 2. The calibration was carried out to ASHRAE Guideline 14-2002 for a calibrated energy model. ASHRAE Guideline 14-2002 addresses accuracy requirements of building performance simulation for energy savings. It requires that the simulated results agree with measured energy data (or billing data) to the following extent: • Normalized mean bias error (NMBE) of ± 5% on an annual basis. • Coefficient of variance for root mean square error “CV(RMSE)” of 15% for monthly measured data.
Fig. 1. NSCC Building.
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Fig. 2. Combined HVAC system diagram.
Table 1 Primary control strategies. Operating scenarios
1
2
3
4
Note
Season Occupancy AHU Supply air setpoint Room thermostat setpoints Water loop return setpoint AHU water coil inlet setpoint
Heating Occupied On 19-21 ◦ C
Heating Unoccupied Off N/A
Cooling Occupied On ∼18 ◦ C
Cooling Unoccupied Off N/A
VFD present Modulated
23 ◦ C
15-27 ◦ C
23 ◦ C
15-27 ◦ C
VFD HP
32 ◦ C
32 ◦ C
28 ◦ C
28 ◦ C
Modulated
30 ◦ C
30 ◦ C
15 ◦ C
15 ◦ C
Sequenced
Table 2 Building occupancy states. State
Hours and days
Occupied Unoccupied
06:00-18:00 Monday – Friday 18:00-06:00 Monday – Friday, All day Saturday, Sunday and Holidays
3. MPC method The MPC strategy method consists of several parts, including software, statistical models, objective function, and execution strategy. 3.1. Software MPC relies on the use of a building model and forecasts of internal and external conditions (e.g., climate) to determine the optimal control strategy to minimize the cost of a desired objective function. For real-time applications this requires a model that can predict its state in a timely manner, and for a variety of initial conditions. While E+ is an accurate and validated building performance simulation software, it has several limitations which inhibit its execution of MPC in real-time for building operations,
including: inability to specify initial conditions zone conditions; use of a number of warm-up days zone pre-conditioning; long execution time relative to simpler models (such as statistical models); and no embedded feature for continuous update of control strategies. Therefore it is necessary to couple it with a second program to perform MPC. E+ has a built in feature called ExternalInterface which has been designed for linking E+ with other computing software for advanced control applications. When using the ExternalInterface feature, a co-simulation is performed, where information is passed for every timestep execution of E+ to the co-simulation program. The co-simulation program then performs the MPC analysis and sends new control setpoints to E+, which replace the original or previous values. The program Building Controls Virtual Test Bed (BCVTB) [11] was chosen for conducting co-simulation with E+. BCVTB is a Java based program that utilizes the Ptolemy II programming language for actor-oriented design. BCVTB has native support for many software packages including Matlab, Modelica, E+, TRNSYS, etc. For the implementation of the MPC algorithm, the statistical computing software R [12] was chosen for its statistical modelling tools. While BCVTB does not natively support R, it does allow users to call custom programs and pass information to them as input arguments (similar to command line arguments), and reading the program outputs. By using this feature, R can be connected to BCVTB using Rscript to call the R MPC function. Fig. 3 shows the flow of information between E+ and R in BCVTB, with the StringSubstring, ExpressionToToken, ElementsToArray, and ArrayToMatrix blocks used to convert the output from R to a usable form for E+, while similar functionality is performed in the R code for the E+ output. This setup allows for E+ to act as a virtual building from with the R based MPC can receive sensor data and forecasts, determine optimized control strategy setpoints, and return these to E+. For clarity: E+ represents the “real building”; BCVTB connects the “real building” sensors and controllers to R, which has inbuilt a statistical model of the building and the MPC optimizer; after execution, the new control strategy setpoints are sent back from R through BCVTB to E+, so the “real building” can continue to execute (i.e., simulate). This setup allows for testing prior to installation on a building and work as a proof of concept platform.
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Fig. 3. BCVTB Interface between E+ and R.
W= 1 1 0
3.2. Objective function, solution options, and optimizer strategy In order to develop the MPC strategy, several parameters were defined. The first is the choice of statistical building model to be used in the MPC. While E+ is used as the “real building” for providing feedback, a second, fast executing model is needed to so as to examine the effects of applying many different control strategies, with reasoning discussed in Section 3.1. To overcome these limitations a statistical building response model (BRM) was developed, with details outlined in Section 4. Secondly, it was decided to only control the zone level temperature setpoints. These were chosen as they are the parameters that determine if space conditioning is needed, and how much conditioning is required. The objective function is designed to minimize energy use, while maintaining a temperature of 23 ◦ C in the building during occupied hours (08:00 and 18:00), such as in [13]. The entire building was assumed to be represented by a single average zone temperature. This was done to minimize computational demands, because the optimization of all 32 zones individually would expand the decision space by a factor of 31. To achieve the desired objective function, a reference value (variable r) for building electricity consumption and thermal energy consumption was set to 0, with the reference indoor temperature set to 23 ◦ C to match the current thermal comfort metric used by the building (Eq. (1)). The vector r represents the electrical consumption reference, thermal energy consumption reference, and the temperature reference. The energy references are set to 0 for minimization, while the temperature reference of 23 ◦ C was chosen to match the existing building comfort criteria. The error (variable E) between the predictions and reference values (Eq. (2)) is required for computing the objective function. To ensure the thermal comfort requirement of 23 ◦ C during occupancy, the square temperature error was scaled by a factor 1018 (Eq. (3), variable W) during occupied hours to create a piece-wise weighting factor [14] so that temperature became the dominant term in the cost function for daytime operation, so as to ensure thermal comfort. The magnitude of the scaling factor is large as the building consumes energy at a rate of 1 MW for thermal energy during peak load. The cost is then calculated as the square of the error times the weighting function (Eq. (4)).
r= 0
0
23
E = predictions − r
(1) (2)
W = 1 1 10
for 18 : 00 to 08 : 00 unoccupied periods
for 8 : 00 to 18 : 00 occupied periods
C = E2 × W
(3a) (3b) (4)
For brute force optimization (such as in [15]), a set of control options needs to be specified along with the number of timesteps to look ahead. These two parameters determine the number of calculations required to determine the total cost. The optimizer has been limited to 3 options (low, medium, and high temperatures) and 8 step look ahead (2 h at 15 min timesteps), which lead to 6561 (38 ) calculations per timestep. Because of the exponent relationship, an expansion on the number of options from 3 to 4 increases the calculations to 65,536, where an increase in look ahead from 8 to 9 expands the calculations to 19,683. For the optimization of every zone with 3 unique options and 8 step look ahead, the MPC would compute 7.2 × 1015 calculations. To calculate a total cost for an 8 timestep horizon, the cost of each individual timestep is summed to create a total cost (Eq. (5)). The search function is limited in that if multiple global minima exist, it will select the first one that it encounters and use those values. CTotal = C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8
(5)
The goals of the MPC algorithm is to maintain thermal comfort (23 ◦ C while occupied) while minimizing energy consumption. To achieve this the optimizer effectively performs two functions: optimized HVAC start time and optimized daytime setpoints. The present control strategy of the building is to start the HVAC equipment at 06:00 every morning in order to achieve the thermal comfort requirement for 08:00. However, on most days the start time can be delayed and still achieve thermal comfort by 08:00. An example of this is shown in Fig. 4 with the traditional RBC as red and an optimized approach in green. The energy savings is the difference highlighted by the 2 cross hatched boxes, one by the delay in conditioning, and one for the reduction in peak energy use by utilizing available solar energy and potentially increased ambient conditions. Note that in both cases, occupancy comfort (23 ◦ C) is achieved by 08:00. The optimizer control options are subdivided into 3 groups: morning hours heating, morning hours cooling, and daytime hours. A pair of filters are used to determine which option set is used. The first filter is time of day, where between 08:00 and 18:00 the daytime options are used. The second filter is outdoor air temperature,
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Fig. 4. Optimized start time.
Table 3 Fixed pair optimizer setpoint options.
between the limits, and only conditions to keep the building at these extremes.
Morning Heating
Morning Cooling
Daytime
Option
Heating
Cooling
Heating
Cooling
Heating
Cooling
1 2 3
15 23 23
27 27 23
15 15 23
27 23 23
22 23 24
22 23 24
Table 4 Deadband optimizer setpoint options. Morning Heating
Morning Cooling
Daytime
Option
Heating
Cooling
Heating
Cooling
Heating
Cooling
1 2 3
15 23 23
27 27 23
15 15 23
27 23 23
22 22.5 23
23 23.5 24
such that a value 23 ◦ C or less uses morning heating options, values above 23 ◦ C use morning cooling values. The values of the setpoints are derived from the current setback temperatures (15 ◦ C and 27 ◦ C) and current daytime operating temperature (23 ◦ C). Two different scenarios are considered: (1) A fixed pair of setpoints, as shown in Table 3. Note that the daytime heating and cooling setpoints (boldface) are identical for a pair. Thus heating and cooling can be immediately applied to insure the temperature stays precisely at the value. (2) A deadband pair of setpoints, as shown in Table 4. In this daytime case the setpoints for heating and cooling are different, allowing the temperature to float between them without active heating or cooling. This allows for the use of the buildings thermal dynamics in maintaining comfort. To reduce computational time additional filters were put in place. Filters were used for weekends and overnight hours (18:15–05:45), where the zone setpoints were set to be 15 ◦ C for heating and 27 ◦ C for cooling. This was done as these are the maximum bands employed by the RBC, and represent the optimal solution for minimal energy consumption when there is no need for thermal control. During this period the building can float
4. Statistical building response model As described in Section 3, a simplified model that can be fed a palette of current and desired future conditions is required for the implementation of MPC due to the limitations of E+. Ideally, a building would have years of measurement information to use for the design of both a detailed E+ model as well as a simplified model. However, most buildings have limited measured data available, and are often limited to monthly energy billing data. These allow an engineer to develop a calibrated model in E+ to ASHRAE Guideline 14-2002 [24]. This model can then be simulated for multiple years of climate data, and various control strategies to generate data for the development of a simplified statistical model. Some model types are resistive capacitive networks [16,17], neural networks [18,19], or other black (or grey) box statistical methods [20,21]. A statistical BRM is created using the Random Forest package for R developed by Breiman and Cutler [22]. The Random Forest model was chosen as it provided the best accuracy when compared with linear regression and neural network models for the same data. The model takes as inputs the time of day, the type of day (workday vs non workday) the current environment conditions (temperature, humidity, wind speed, wind direction, direct solar radiation, and diffuse solar radiation), current average building temperature, and thermal energy usage rate for the past timestep (15 min), electricity usage since the previous timestep, future environment conditions (1 timestep ahead), and future zone setpoints (1 timestep ahead). These values are chosen based on the ability to measure these values in the building management system, with a sensor outline in Table 5. From this set of information, the model produces estimates for thermal energy usage rate for the next timestep, electricity usage during the next timestep, and building temperature at the next timestep. The variables time of day and type of day are used to estimate the internal heat generation loads in the building as opposed to using a specific variable for this task. This is done as it can be difficult to measure sources of internal heat generation (such as number of occupants), but they tend to follow time of day patterns [23]. To generate the data needed to create a BRM, a calibrated E+ model was run with various control strategies for the same
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Table 5 Building measurements for MPC. Sensor
Thermostats
AHU flow meter
Weather station
Condensate Return
Electricity Meters
Measurement
Zone temperatures throughout the building
Supply air flow rate
Ambient temperature, humidity, solar radiation and wind
Thermal energy usage
Whole building electricity usage
5. Results
Cooling Setpoint
24 19 14 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Fig. 5. Sample training setpoint profile.
climate year, producing a group of 18 yearlong training data files. The various control strategies would alter the morning startup time between the current rule based start time and the nominal building occupancy, and consist of randomization on either the heating (9 sets) or cooling setpoint (9 sets) during unoccupied periods. The randomization is required for the model to understand the difference between heating and cooling as the statistical model has no physics incorporated. During the occupied period a deadband of 1 ◦ C was applied, which varied up and down based on a probability weighted scheme to create variations for the BRM, within a temperature band of 20 ◦ C and 24 ◦ C. These variations are needed to allow the BRM to provide the optimization function accurate predictions for deviating from the existing rule based control. An example of one of the training data profiles is given in Fig. 5, where the cooling setpoint is randomized to allow the model to distinguish the differences between heating and cooling. After training the BRM from the 18 sets, it was tested using 9 test sets for validation. The test sets include the existing rule based control, a halfway jump in setpoints, a morning ramp, and various midday strategies encompassed by the randomization values in the training data. The results for a winter week (6–10 February 2013) and a summer week (7–11 August 2013) are shown in Fig. 6, and indicate that the BRM does indeed give good representation of “real building” HVAC energy demand (given as E+ values). The fit of the model is also confirmed by the R2 values of 0.99 for electricity and temperature, and 0.97 for thermal energy.
A detailed results comparison is necessary to determine the performance gain of using a MPC approach compared to a traditional RBC approach, and to determine the value of adding a deadband to the MPC setpoint options. Table 6 and Fig. 7 outline the HVAC electrical consumption, thermal energy consumption and total HVAC energy consumption between the existing RBC, fixed setpoint MPC, and deadband MPC scenarios. The data shows the use of a fixed setpoint MPC approach provides no savings in HVAC electricity consumption. This is in part because the only HVAC electrical load affected by adjusting zone level temperatures is the zone level heat pumps, which are almost constantly running under a fixed setpoint scenario. A reduction of 10% annual thermal energy use is achieved. The total HVAC energy reduction of 4% for the fixed setpoint pair MPC is achieved by delayed start time of building conditioning. For the deadband setpoints MPC, a 10% HVAC electricity consumption is realized. This is due to the deadband allowing for the heat pumps to turn off while the temperature in zones fluctuates between the heating and cooling value. A reduction in thermal energy of 6% is lower than that for the fixed pair setpoint case. This is due to when using fixed setpoints, more energy is returned to the common heat pump loop from cooling than when a deadband is applied. A total HVAC energy reduction of 9% is achieved for deadband MPC, because of delayed start time of building conditioning, wider occupied temperature band, and enhanced used of passive solar stored in building thermal mass. To help understand where the savings are generated, Figs. 8 and 9 show the average (solid line) and minimum/maximum (dashed line) seasonal morning temperature for RBC, persistence fixed setpoint MPC, and persistence deadband MPC. The winter plot highlights that the MPC options allow the temperature to stay lower with the ramp action beginning strongly at 07:45, and the building is up to temperature for 08:00. In the summer case, the MPC allows the building to naturally warm up and even overheat slightly before cooling engages and brings the building to the desired temperature of 23 ◦ C.
6-7 February 2013 E+ Thermal Energy
BRM Thermal Energy
7-8 August 2013 E+ Electricity
BRM Electricity
1200
Power (kW)
1000 800 600 400 200 00:00 04:00 08:00 12:00 16:00 20:00 00:00 04:00 08:00 12:00 16:00 20:00
0 00:00 04:00 08:00 12:00 16:00 20:00 00:00 04:00 08:00 12:00 16:00 20:00
Temperature (°C)
Heang Setpoint 29
Fig. 6. BRM performance verification for 6-10 February 2013 (winter) and 7-8 August 2013 (summer).
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Table 6 Fixed setpoint annual energy comparison (kWh).
HVAC Electricity Thermal Energy Total HVAC Energy
RBC
Persistence Fixed Setpoint MPC
Persistence Fixed Setpoint MPC %
Persistence Deadband Setpoint MPC
Persistence Deadband Setpoint MPC %
1349614 673597 2023211
1345149 603516 1948665
100% 90% 96%
1208014 633170 1841184
90% 94% 91%
RBC HVAC Electricity
RBC Thermal Energy
Persistence Fixed Setpoint MPC HVAC Electricity
Persistence Fixed Setpoint MPC Thermal Energy
Persistence Deadband Setpoint MPC HVAC Electricity
Persistence Deadband Setpoint MPC Thermal Energy
350000
Energy(kWh)
300000 250000 200000 150000 100000 50000 0 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Fig. 7. Monthly fixed setpoint HVAC energy usage comparison.
Winter
Temperature (°C)
RBC 25
Persistence Fixed Setpoint MPC
Persistence Deadband Setpoint MPC
23 21 19 17 08:45
08:30
08:15
08:00
07:45
07:30
07:15
07:00
06:45
06:30
06:15
06:00
05:45
05:30
05:00
05:15
15
Fig. 8. Average winter morning temperature (solid line) with maximum and minimums for the season (dashed lines).
Summer Persistence fixed setpoints MPC
RBC Persistence deadband setpoints MPC
25 23
Persistence
Persistence DB
Frequency
Temperature (°C)
RBC 27
21
21
19
21.5
22
22.5 23 23.5 Temperature (°C)
24
24.5
25
17 08:45
08:30
08:00
08:15
07:45
07:30
07:15
07:00
06:45
06:30
06:15
06:00
05:45
05:30
05:15
05:00
15
Fig. 9. Average summer morning temperature (solid line) with maximum and minimums for the season (dashed lines).
A comparison of occupancy comfort during occupied periods (08:00–18:00, Monday–Friday) can be found in Fig. 10, which shows that RBC manages to maintain 23 ◦ C more consistently than both fixed setpoint and deadband setpoint persistence MPC techniques. The deadband setpoint persistence technique performs
Fig. 10. Fixed setpoints occupancy comfort comparison (08:00 – 18:00, Monday – Friday).
better than fixed setpoint persistence in that it has a larger number of values in the bins for 23 ◦ C and 23.5 ◦ C that correspond to the defined thermal comfort metric. Fig. 11 illustrates the difference the individual 32 zone temperatures for a winter day and summer day for RBC and persistence deadband setpoint MPC control strategies. The figures highlight the challenge of attempting to optimize for the entire building at once, as some zones heat up faster than others, and illustrates the
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Fig. 11. All zone temperatures (colors) shown with setpoints (red) for winter (left) and summer (right) periods.
potential for savings by moving to a zone level based optimization strategy. The trends also help illustrate why the persistence deadband MPC case has constantly fluctuating setpoints, as the control is based on the average of all zone temperatures, and this fluctuates a small amount with time. 6. Conclusions and recommendations The results of the MPC work outlined in this paper show that a reduction in building HVAC energy use of 4% can be achieved with fixed setpoint pairs, but at the expense of fluctuating temperatures during the daytime hours. The 4% savings are comprised of a 10% reduction in thermal energy and no reduction in HVAC electricity. A second comparison is made between the original RBC (fixed at 23 ◦ C) and the persistence MPC with a setpoint deadband. This is the likely scenario for implementing MPC technology on existing buildings. In this case, the MPC gives a 9% total HVAC energy reduction, with a 6% reduction in thermal energy and 10% HVAC electricity reduction. A key trend from this is that while total savings increased, the thermal energy actually had smaller savings than when all scenarios used fixed pairs. This occurs because the strict conditions of using fixed temperature setpoint pairs adds energy to the heat pump loop from zones that would heat beyond the setpoint, whereas with a dead band the zone is allowed to freely rise in temperature until the cooling value is reached. A simplified thermal comfort evaluation was conducted to compare the results of RBC to the persistence fixed setpoint MPC and persistence deadband MPC. RBC had the best occupancy comfort when considering the strict 23 ◦ C target, followed by persistence deadband MPC, and then persistence fixed setpoint MPC for the occupied period (08:00–18:00, Monday–Friday). The initial savings indicated by the work are promising due to the use a persistence based forecast methodology, a small solution space and limited forecast horizon. It also employed a rather simple thermal comfort of precisely 23 ◦ C. Due to these limitations, there exists areas for improving the MPC results:
• Expand the objective function: this includes cost savings (energy and demand charges, time of use energy pricing [13]), greenhouse gas emission reduction (e.g., electricity vs thermal savings), and occupancy comfort (e.g., Predicted Mean Vote of Percentage of People Dissatisfied [25]). • Expand the solution space: optimize on each zone instead of the average zone; provide additional setpoint solutions (more than 3); consider additional periods such as overnight free-cooling. • Enhance the forecast: use of high-accuracy site-specific forecasting will advantage MPC compared with persistence forecast methods; it also enhances confidence in using a longer forecast horizon. • Faster optimization techniques: brute force is slow but accurate; genetic algorithms, particle swarms, and others [7,26] should allow for an expanded forecast horizon and solution palette with a reasonable increase in computing power and time. Acknowledgements The authors gratefully acknowledge the assistance and support provided by Green Power Labs Inc. throughout this research. In addition, the authors appreciate major funding support provided by the Atlantic Canadian Opportunities Agency supporting innovative economic growth in Canada’s Atlantic Provinces. Additional funding was provided by MITACS (IT03561). References [1] Natural Resources Canada, Energy Use Data Handbook 1990–2010, 2013. [2] Whelton, M. G. Cost performance of energy efficiency measures in residential retrofit projects. IGLC 2012—20th Conference of the International Group for Lean Construction. (2012). [3] S. Wang, Z. Ma, Supervisory and optimal control of building HVAC systems: a review, HVAC R Res. 14 (1) (2008) 3–32. [4] Erickson, L. Varick, Occupancy modeling and prediction for building energy management, ACM Trans. Sensor Netw. 10 (3) (2014), 42:1–42:28. [5] Guiles Jr., G. Ellis, Building professional accreditation, construction quality control and better buildings, ASHRAE Transactions 117 (1) (2011) 170–177.
232
T. Hilliard et al. / Energy and Buildings 111 (2016) 224–232
[6] S.C. Bengea, A.D. Kelman, F. Borrelli, R. Taylor, S. Narayanan, Implementation of model predictive control for an HVAC system in a mid-size commercial building, HVAC and R Research 20 (1) (2014) 121–135, http://dx.doi.org/10. 1080/10789669.2013.834781. [7] C.D. Corbin, G.P. Henze, P. May-Ostendorp, A model predictive control optimization environment for real-time commercial building application, Journal of Building Performance Simulation 6 (3) (2013) 159–174, http://dx. doi.org/10.1080/19401493.2011.648343. [8] A. Afram, F. Janabi-Sharifi, Theory and applications of HVAC control systems A review of model predictive control (MPC), Building and Environment 72 (2014), http://dx.doi.org/10.1016/j.buildenv.2013.11.016, 343e355-343e355. [9] T. Hilliard, M. Kavgic, L. Swan, Model predictive control for buildings: Trends and opportunities, Advances in Building Energy Research. (2015), Accepted June 8, 2015 (TAER-2014-0071.R1). [10] Nova Scotia Community College. Waterfront Campus. http://www.nscc.ca/ explorenscc/campuses/waterfront/(accessed 04.04.2014). [11] Building Controls Virtual Test Bed. Lawrence Berkeley National Laboratory. https://simulationresearch.lbl.gov/bcvtb (accessed 06.08.2015). [12] R. The R Project for Statistical Computing. http://www.r-project.org/(accessed 05.04.14). [13] M. Avci, M. Erkoc, A. Rahmani, S. Asfour, Model predictive HVAC load control in buildings using real-time electricity pricing, Energy and Buildings 60 (2013) 199–209, http://dx.doi.org/10.1016/j.enbuild.2013.01.008. [14] P. Morosan, R. Bourdais, D. Dumur, J. Buisson, Building temperature regulation using a distributed model predictive control, Energy and Buildings 42 (9) (2010) 1445–1452, http://dx.doi.org/10.1016/j.enbuild.2010. 03.014. [15] J. Zhao, K.P. Lam, B.E. Ydstie, EnergyPlus model based predictive control (EPMPC) by using Matlab/Simulink and MLE+, in: 13th Conference of International Building Performance Simulation Association, Chambéry, France, 2013, pp. 2466–2473, August 26-28, 2013. [16] B. Lehmann, D. Gyalistras, M. Gwerder, K. Wirth, S. Carl, Intermediate complexity model for model predictive control of integrated room automation, Energy and Buildings 58 (2013) 250–262, http://dx.doi.org/10. 1016/j.enbuild.2012.12.007.
[17] J. Siroky, F. Oldewurtel, J. Cigler, S. Privara, Experimental analysis of model predictive control for an energy efficient building heating system, Applied Energy 88 (9) (2011) 3079–3087, http://dx.doi.org/10.1016/j.apenergy.2011. 03.009. [18] Neto, H. Alberto, Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, Energy and buildings 40 (12) (2008) 2169–2176. [19] Tao. Lu, A hybrid numerical-neural-network model for building simulation: A case study for the simulation of unheated and uncooled indoor temperature, Energy and buildings 86 (2015) 723–734. [20] S. Privara, J. Siroky, L. Ferkl, J. Cigler, Model predictive control of a building heating system: The first experience, Energy and Buildings 43 (2–3) (2011) 564–572, http://dx.doi.org/10.1016/j.enbuild.2010.10.022. [21] S.R. West, J.K. Ward, J. Wall, Trial results from a model predictive control and optimisation system for commercial building HVAC, Energy and Buildings 72 (2014) 271–279, http://dx.doi.org/10.1016/j.enbuild.2013.12.037. [22] A. Liaw, M. Wiener, Classification and Regression by randomForest, R News 2 (3) (2002) 18–22. [23] T.L. McKinley, Identification of building model parameters and loads using on-site data logs, Governing 10 (2008) 3. [24] ASHRAE Guideline 14-2002 – Measurement of Energy and Demand Savings, ASHRAE. [25] B. Coffey, Approximating model predictive control with existing building simulation tools and offline optimization, Journal of Building Performance Simulation 6 (3) (2013) 220–235, http://dx.doi.org/10.1080/19401493.2012. 737834. [26] P. May-Ostendorp, G.P. Henze, C.D. Corbin, B. Rajagopalan, C. Felsmann, Model-predictive control of mixed-mode buildings with rule extraction, Building and Environment 46 (2) (2011) 428–437, http://dx.doi.org/10.1016/j. buildenv.2010.08.004. [27] US DOE. “EnergyPlus Energy Simulation Software”. http://apps1.eere. energy.gov/buildings/energyplus/(accessed 04.06.2014). [28] G. Zheng, Y. Jing, Thermodynamics performance study on water source heat pump in variant operating condition, Energy and Environment 20 (4) (2009) 517–532.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Development of a whole building model predictive control strategy for a LEED silver community college Trent Hilliard a,∗ , Lukas Swan a , Miroslava Kavgic a , Zheng Qin b , Pawan Lingras c a b c
Department of Mechanical Engineering, Dalhousie University, PO BOX 15000, Halifax, NS, Canada B3H 4R2 Green Power Labs, 1 Research Drive, Dartmouth, NS, Canada, B2Y 4M9 Faculty of Computer Science, St. Mary’s University, 923 Robie Street, Halifax NS, Canada, B3H 3C3
a r t i c l e
i n f o
Article history: Received 14 August 2015 Received in revised form 18 November 2015 Accepted 19 November 2015 Available online 23 November 2015 Keywords: Model predictive control Thermal comfort Energy efficiency
a b s t r a c t A model predictive control strategy method is developed for a LEED silver community college building that contains an advanced common loop heat pump HVAC system. MPC is performed using persistence based climate forecasting with two scenarios examined: fixed temperature setpoint pairs; and a deadband of 1 ◦ C between the heating/cooling setpoint. EnergyPlus acts as the “real” building and is linked with R software for model predictive control via the Building Controls Virtual Test Bed. A statistical based building response model is created in R using training data from a calibrated EnergyPlus model. A brute force optimization strategy is used for solving the objective function. A piece-wise objective function maintains thermal comfort during occupied periods with a focus on reducing energy consumption during other periods. Results when using fixed setpoint pairs exhibit a 4% reduction in HVAC energy consumption, and deadband setpoints exhibit a 9% reduction in HVAC energy consumption, compared with reactive rule based control. Of interest is that the type of energy savings (thermal energy vs electricity) varies depending upon the setpoint options. The results are promising given the strict thermal comfort requirement employed, minimal search space, use of simplistic persistence forecasting, and the sophisticated HVAC system. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Commercial/institutional and residential buildings are the second largest consumer of energy in the Canada, accounting for 29% of all energy consumed nationwide [1]. Of the energy used in buildings, 55% is for space heating and 3% for space cooling, resulting in a total of 58% used for space conditioning. In Canada, space conditioning of these building types uses 1412 PJ of energy annually, and thus is a massive market where even small gains across the entire sector constitute large energy savings. Due to the vast amount of energy and cost associated with buildings, it is important to find methods to reduce energy consumption. While energy efficient retrofits are one path to achieve savings, they often have high capital costs, and take significant time/effort to implement [2]. A second approach is to modify how the existing equipment is being utilized in a building through the implementation of new control strategies. Within a building the controls can be broken down to two main categories: local controllers that
∗ Corresponding author. Tel.: +1 902 499 8574; fax: +1 902 423 6711. E-mail address: [email protected] (T. Hilliard). http://dx.doi.org/10.1016/j.enbuild.2015.11.051 0378-7788/© 2015 Elsevier B.V. All rights reserved.
operate a specific piece of equipment (such as fresh air dampers), and supervisory controls that supply target values that drive local controllers [3]. While many local controllers are finely tuned to provide reference value tracking from the manufacturer, supervisory controllers are often preprogrammed based on expected occupancy periods, internal loads, and envelope characteristics. Often times these parameters can change over time (such as a shift in building clientele), or are assumptions that do not account for issues during construction [4], [5]. Due to these factors, supervisory control at a building level is an ideal location for advancements in control logic that is adaptable to changing conditions. Model predictive control (MPC) is such a strategy. MPC is a branch of control theory that utilizes a model of a system or process that is subject to constraints, and attempts to find an optimal solution to an objective function based on current and forecast values. The optimal solution is typically found by solving an objective function (typically a cost minimization) [6], with constraints that limit the range of output control values. A prediction horizon is used, along with a specified timestep to allow the optimizer to look into the future and consider dynamic effects so that an optimal solution is found over the entire forecast, not just the current timestep [7]. The use of forecast information allows the
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education building servicing 2500 students. The building consists of 5 occupied floors, with a 5400 m2 footprint, for a total of 27,000 m2 of conditioned floor space, and is operated year round. Zone types include classrooms, open space, atriums, laboratories, workshops, cafeteria, offices, and mechanical rooms. The HVAC system for the building consists of four main components:
Nomenclature AHU BCVTB BRM C E E+ HP HVAC MPC r R VFD W
225
air handling unit building controls virtual test bed building response model cost of optimization error between prediction and desired reference EnergyPlus heat pump heating, ventilation, and air conditioning model predictive control reference values for optimization R statistical computing software variable frequency drive MPC weighting matrices
• Main water loop which links the steam converter (serviced by a district heat system) and cooling tower to the heat pumps servicing the air handling units (AHU) and zone level heat pumps. • AHU glycol loop which is controlled by water to water heat pumps on the main water loop and feeds the coils in the AHUs. • Main air loop which provides supply and return air to the zones, including the use of a heat wheel. • Zone level water to air heat pumps which transfer heat between the main water loop and the zone air.
system to adapt to changing climatic conditions, or changing internal dynamics, depending on how the problem is defined. Several review papers on MPC for buildings have been recently published [3,8,9]. Readers are encouraged to investigate these as they detail the evolution of MPC for buildings. In this paper a persistence based forecast MPC strategy is used to reduce HVAC energy consumption of a whole building. The persistence forecast consists of measuring the current value of a forecast variable, and assumes it does not change throughout the forecast horizon. Two different control strategies are used for the MPC: one with fixed setpoint pairs (e.g., heating and cooling values are the same) and one with a deadband of 1 ◦ C between the heating and cooling value. The results of the MPCs are compared to the existing rule based control (RBC) for the building outlined in Section 2. The paper shows that for a new LEED silver certified building, MPC does offer energy savings compared to the existing RBC strategy. The paper also introduces the use of R statistical software linked with EnergyPlus (E+) [27] for modeling and objective function calculations. Section 2 describes the building that has been modelled for the development of the MPC. Section 3 outlines the MPC method, starting with the software used, followed by the objective function, system timestep and forecast horizon. Section 4 outlines the work in developing a statistical black box model for use within the MPC, and a verification analysis is provided to validate the modelling methodology. Section 5 discusses the results of the 2 MPC scenarios in comparison to the existing RBC control strategy, highlighting the value provided by MPC. Section 6 concludes the paper with a discussion of the results, and suggestions to further improve the results of MPC in this building. 2. Building The Harbourside Wing of the Nova Scotia Community College Waterfront Campus, shown in Fig. 1, is a LEED silver certified [10]
Fig. 2 shows how these systems are interconnected. The main water loop feeds the zone level heat pumps and the AHU heat pumps which are connected to the air stream via the AHU glycol loop. The AHU glycol loop heats/cools the incoming fresh air to the building before it is delivered to the various zones. The zone level heat pumps provide the final conditioning to the zones, and will work to make up any deficits in heating/cooling from the main air loop. The main water loop allows for energy transfer between zones as the heat pumps push and pull energy from the loop. The loop is maintained between 28 ◦ C and 32 ◦ C to allow for optimal heat pump coefficient of performance, with [28] outlining thermodynamically the effects of inlet water temperature on heat pump coefficient of performance. The existing control strategy at the NSCC Harbourside Wing has four operating scenarios as outlined in Table 1. There are two strategies for each season (heating and cooling) based on building occupancy (described in Table 2). Each strategy has a unique palette of setpoints as outlined, while the type of control is listed under “Note”. A calibrated energy model of the building was created in E+ using as-built design drawings, air balance test results, operating strategies described above from the building operator, and the monthly energy billing data (thermal energy and electricity for 1 year). The model consists of 32 thermal zones and a detailed HVAC model as shown in Fig. 2. The calibration was carried out to ASHRAE Guideline 14-2002 for a calibrated energy model. ASHRAE Guideline 14-2002 addresses accuracy requirements of building performance simulation for energy savings. It requires that the simulated results agree with measured energy data (or billing data) to the following extent: • Normalized mean bias error (NMBE) of ± 5% on an annual basis. • Coefficient of variance for root mean square error “CV(RMSE)” of 15% for monthly measured data.
Fig. 1. NSCC Building.
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Fig. 2. Combined HVAC system diagram.
Table 1 Primary control strategies. Operating scenarios
1
2
3
4
Note
Season Occupancy AHU Supply air setpoint Room thermostat setpoints Water loop return setpoint AHU water coil inlet setpoint
Heating Occupied On 19-21 ◦ C
Heating Unoccupied Off N/A
Cooling Occupied On ∼18 ◦ C
Cooling Unoccupied Off N/A
VFD present Modulated
23 ◦ C
15-27 ◦ C
23 ◦ C
15-27 ◦ C
VFD HP
32 ◦ C
32 ◦ C
28 ◦ C
28 ◦ C
Modulated
30 ◦ C
30 ◦ C
15 ◦ C
15 ◦ C
Sequenced
Table 2 Building occupancy states. State
Hours and days
Occupied Unoccupied
06:00-18:00 Monday – Friday 18:00-06:00 Monday – Friday, All day Saturday, Sunday and Holidays
3. MPC method The MPC strategy method consists of several parts, including software, statistical models, objective function, and execution strategy. 3.1. Software MPC relies on the use of a building model and forecasts of internal and external conditions (e.g., climate) to determine the optimal control strategy to minimize the cost of a desired objective function. For real-time applications this requires a model that can predict its state in a timely manner, and for a variety of initial conditions. While E+ is an accurate and validated building performance simulation software, it has several limitations which inhibit its execution of MPC in real-time for building operations,
including: inability to specify initial conditions zone conditions; use of a number of warm-up days zone pre-conditioning; long execution time relative to simpler models (such as statistical models); and no embedded feature for continuous update of control strategies. Therefore it is necessary to couple it with a second program to perform MPC. E+ has a built in feature called ExternalInterface which has been designed for linking E+ with other computing software for advanced control applications. When using the ExternalInterface feature, a co-simulation is performed, where information is passed for every timestep execution of E+ to the co-simulation program. The co-simulation program then performs the MPC analysis and sends new control setpoints to E+, which replace the original or previous values. The program Building Controls Virtual Test Bed (BCVTB) [11] was chosen for conducting co-simulation with E+. BCVTB is a Java based program that utilizes the Ptolemy II programming language for actor-oriented design. BCVTB has native support for many software packages including Matlab, Modelica, E+, TRNSYS, etc. For the implementation of the MPC algorithm, the statistical computing software R [12] was chosen for its statistical modelling tools. While BCVTB does not natively support R, it does allow users to call custom programs and pass information to them as input arguments (similar to command line arguments), and reading the program outputs. By using this feature, R can be connected to BCVTB using Rscript to call the R MPC function. Fig. 3 shows the flow of information between E+ and R in BCVTB, with the StringSubstring, ExpressionToToken, ElementsToArray, and ArrayToMatrix blocks used to convert the output from R to a usable form for E+, while similar functionality is performed in the R code for the E+ output. This setup allows for E+ to act as a virtual building from with the R based MPC can receive sensor data and forecasts, determine optimized control strategy setpoints, and return these to E+. For clarity: E+ represents the “real building”; BCVTB connects the “real building” sensors and controllers to R, which has inbuilt a statistical model of the building and the MPC optimizer; after execution, the new control strategy setpoints are sent back from R through BCVTB to E+, so the “real building” can continue to execute (i.e., simulate). This setup allows for testing prior to installation on a building and work as a proof of concept platform.
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Fig. 3. BCVTB Interface between E+ and R.
W= 1 1 0
3.2. Objective function, solution options, and optimizer strategy In order to develop the MPC strategy, several parameters were defined. The first is the choice of statistical building model to be used in the MPC. While E+ is used as the “real building” for providing feedback, a second, fast executing model is needed to so as to examine the effects of applying many different control strategies, with reasoning discussed in Section 3.1. To overcome these limitations a statistical building response model (BRM) was developed, with details outlined in Section 4. Secondly, it was decided to only control the zone level temperature setpoints. These were chosen as they are the parameters that determine if space conditioning is needed, and how much conditioning is required. The objective function is designed to minimize energy use, while maintaining a temperature of 23 ◦ C in the building during occupied hours (08:00 and 18:00), such as in [13]. The entire building was assumed to be represented by a single average zone temperature. This was done to minimize computational demands, because the optimization of all 32 zones individually would expand the decision space by a factor of 31. To achieve the desired objective function, a reference value (variable r) for building electricity consumption and thermal energy consumption was set to 0, with the reference indoor temperature set to 23 ◦ C to match the current thermal comfort metric used by the building (Eq. (1)). The vector r represents the electrical consumption reference, thermal energy consumption reference, and the temperature reference. The energy references are set to 0 for minimization, while the temperature reference of 23 ◦ C was chosen to match the existing building comfort criteria. The error (variable E) between the predictions and reference values (Eq. (2)) is required for computing the objective function. To ensure the thermal comfort requirement of 23 ◦ C during occupancy, the square temperature error was scaled by a factor 1018 (Eq. (3), variable W) during occupied hours to create a piece-wise weighting factor [14] so that temperature became the dominant term in the cost function for daytime operation, so as to ensure thermal comfort. The magnitude of the scaling factor is large as the building consumes energy at a rate of 1 MW for thermal energy during peak load. The cost is then calculated as the square of the error times the weighting function (Eq. (4)).
r= 0
0
23
E = predictions − r
(1) (2)
W = 1 1 10
for 18 : 00 to 08 : 00 unoccupied periods
for 8 : 00 to 18 : 00 occupied periods
C = E2 × W
(3a) (3b) (4)
For brute force optimization (such as in [15]), a set of control options needs to be specified along with the number of timesteps to look ahead. These two parameters determine the number of calculations required to determine the total cost. The optimizer has been limited to 3 options (low, medium, and high temperatures) and 8 step look ahead (2 h at 15 min timesteps), which lead to 6561 (38 ) calculations per timestep. Because of the exponent relationship, an expansion on the number of options from 3 to 4 increases the calculations to 65,536, where an increase in look ahead from 8 to 9 expands the calculations to 19,683. For the optimization of every zone with 3 unique options and 8 step look ahead, the MPC would compute 7.2 × 1015 calculations. To calculate a total cost for an 8 timestep horizon, the cost of each individual timestep is summed to create a total cost (Eq. (5)). The search function is limited in that if multiple global minima exist, it will select the first one that it encounters and use those values. CTotal = C1 + C2 + C3 + C4 + C5 + C6 + C7 + C8
(5)
The goals of the MPC algorithm is to maintain thermal comfort (23 ◦ C while occupied) while minimizing energy consumption. To achieve this the optimizer effectively performs two functions: optimized HVAC start time and optimized daytime setpoints. The present control strategy of the building is to start the HVAC equipment at 06:00 every morning in order to achieve the thermal comfort requirement for 08:00. However, on most days the start time can be delayed and still achieve thermal comfort by 08:00. An example of this is shown in Fig. 4 with the traditional RBC as red and an optimized approach in green. The energy savings is the difference highlighted by the 2 cross hatched boxes, one by the delay in conditioning, and one for the reduction in peak energy use by utilizing available solar energy and potentially increased ambient conditions. Note that in both cases, occupancy comfort (23 ◦ C) is achieved by 08:00. The optimizer control options are subdivided into 3 groups: morning hours heating, morning hours cooling, and daytime hours. A pair of filters are used to determine which option set is used. The first filter is time of day, where between 08:00 and 18:00 the daytime options are used. The second filter is outdoor air temperature,
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Fig. 4. Optimized start time.
Table 3 Fixed pair optimizer setpoint options.
between the limits, and only conditions to keep the building at these extremes.
Morning Heating
Morning Cooling
Daytime
Option
Heating
Cooling
Heating
Cooling
Heating
Cooling
1 2 3
15 23 23
27 27 23
15 15 23
27 23 23
22 23 24
22 23 24
Table 4 Deadband optimizer setpoint options. Morning Heating
Morning Cooling
Daytime
Option
Heating
Cooling
Heating
Cooling
Heating
Cooling
1 2 3
15 23 23
27 27 23
15 15 23
27 23 23
22 22.5 23
23 23.5 24
such that a value 23 ◦ C or less uses morning heating options, values above 23 ◦ C use morning cooling values. The values of the setpoints are derived from the current setback temperatures (15 ◦ C and 27 ◦ C) and current daytime operating temperature (23 ◦ C). Two different scenarios are considered: (1) A fixed pair of setpoints, as shown in Table 3. Note that the daytime heating and cooling setpoints (boldface) are identical for a pair. Thus heating and cooling can be immediately applied to insure the temperature stays precisely at the value. (2) A deadband pair of setpoints, as shown in Table 4. In this daytime case the setpoints for heating and cooling are different, allowing the temperature to float between them without active heating or cooling. This allows for the use of the buildings thermal dynamics in maintaining comfort. To reduce computational time additional filters were put in place. Filters were used for weekends and overnight hours (18:15–05:45), where the zone setpoints were set to be 15 ◦ C for heating and 27 ◦ C for cooling. This was done as these are the maximum bands employed by the RBC, and represent the optimal solution for minimal energy consumption when there is no need for thermal control. During this period the building can float
4. Statistical building response model As described in Section 3, a simplified model that can be fed a palette of current and desired future conditions is required for the implementation of MPC due to the limitations of E+. Ideally, a building would have years of measurement information to use for the design of both a detailed E+ model as well as a simplified model. However, most buildings have limited measured data available, and are often limited to monthly energy billing data. These allow an engineer to develop a calibrated model in E+ to ASHRAE Guideline 14-2002 [24]. This model can then be simulated for multiple years of climate data, and various control strategies to generate data for the development of a simplified statistical model. Some model types are resistive capacitive networks [16,17], neural networks [18,19], or other black (or grey) box statistical methods [20,21]. A statistical BRM is created using the Random Forest package for R developed by Breiman and Cutler [22]. The Random Forest model was chosen as it provided the best accuracy when compared with linear regression and neural network models for the same data. The model takes as inputs the time of day, the type of day (workday vs non workday) the current environment conditions (temperature, humidity, wind speed, wind direction, direct solar radiation, and diffuse solar radiation), current average building temperature, and thermal energy usage rate for the past timestep (15 min), electricity usage since the previous timestep, future environment conditions (1 timestep ahead), and future zone setpoints (1 timestep ahead). These values are chosen based on the ability to measure these values in the building management system, with a sensor outline in Table 5. From this set of information, the model produces estimates for thermal energy usage rate for the next timestep, electricity usage during the next timestep, and building temperature at the next timestep. The variables time of day and type of day are used to estimate the internal heat generation loads in the building as opposed to using a specific variable for this task. This is done as it can be difficult to measure sources of internal heat generation (such as number of occupants), but they tend to follow time of day patterns [23]. To generate the data needed to create a BRM, a calibrated E+ model was run with various control strategies for the same
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Table 5 Building measurements for MPC. Sensor
Thermostats
AHU flow meter
Weather station
Condensate Return
Electricity Meters
Measurement
Zone temperatures throughout the building
Supply air flow rate
Ambient temperature, humidity, solar radiation and wind
Thermal energy usage
Whole building electricity usage
5. Results
Cooling Setpoint
24 19 14 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Fig. 5. Sample training setpoint profile.
climate year, producing a group of 18 yearlong training data files. The various control strategies would alter the morning startup time between the current rule based start time and the nominal building occupancy, and consist of randomization on either the heating (9 sets) or cooling setpoint (9 sets) during unoccupied periods. The randomization is required for the model to understand the difference between heating and cooling as the statistical model has no physics incorporated. During the occupied period a deadband of 1 ◦ C was applied, which varied up and down based on a probability weighted scheme to create variations for the BRM, within a temperature band of 20 ◦ C and 24 ◦ C. These variations are needed to allow the BRM to provide the optimization function accurate predictions for deviating from the existing rule based control. An example of one of the training data profiles is given in Fig. 5, where the cooling setpoint is randomized to allow the model to distinguish the differences between heating and cooling. After training the BRM from the 18 sets, it was tested using 9 test sets for validation. The test sets include the existing rule based control, a halfway jump in setpoints, a morning ramp, and various midday strategies encompassed by the randomization values in the training data. The results for a winter week (6–10 February 2013) and a summer week (7–11 August 2013) are shown in Fig. 6, and indicate that the BRM does indeed give good representation of “real building” HVAC energy demand (given as E+ values). The fit of the model is also confirmed by the R2 values of 0.99 for electricity and temperature, and 0.97 for thermal energy.
A detailed results comparison is necessary to determine the performance gain of using a MPC approach compared to a traditional RBC approach, and to determine the value of adding a deadband to the MPC setpoint options. Table 6 and Fig. 7 outline the HVAC electrical consumption, thermal energy consumption and total HVAC energy consumption between the existing RBC, fixed setpoint MPC, and deadband MPC scenarios. The data shows the use of a fixed setpoint MPC approach provides no savings in HVAC electricity consumption. This is in part because the only HVAC electrical load affected by adjusting zone level temperatures is the zone level heat pumps, which are almost constantly running under a fixed setpoint scenario. A reduction of 10% annual thermal energy use is achieved. The total HVAC energy reduction of 4% for the fixed setpoint pair MPC is achieved by delayed start time of building conditioning. For the deadband setpoints MPC, a 10% HVAC electricity consumption is realized. This is due to the deadband allowing for the heat pumps to turn off while the temperature in zones fluctuates between the heating and cooling value. A reduction in thermal energy of 6% is lower than that for the fixed pair setpoint case. This is due to when using fixed setpoints, more energy is returned to the common heat pump loop from cooling than when a deadband is applied. A total HVAC energy reduction of 9% is achieved for deadband MPC, because of delayed start time of building conditioning, wider occupied temperature band, and enhanced used of passive solar stored in building thermal mass. To help understand where the savings are generated, Figs. 8 and 9 show the average (solid line) and minimum/maximum (dashed line) seasonal morning temperature for RBC, persistence fixed setpoint MPC, and persistence deadband MPC. The winter plot highlights that the MPC options allow the temperature to stay lower with the ramp action beginning strongly at 07:45, and the building is up to temperature for 08:00. In the summer case, the MPC allows the building to naturally warm up and even overheat slightly before cooling engages and brings the building to the desired temperature of 23 ◦ C.
6-7 February 2013 E+ Thermal Energy
BRM Thermal Energy
7-8 August 2013 E+ Electricity
BRM Electricity
1200
Power (kW)
1000 800 600 400 200 00:00 04:00 08:00 12:00 16:00 20:00 00:00 04:00 08:00 12:00 16:00 20:00
0 00:00 04:00 08:00 12:00 16:00 20:00 00:00 04:00 08:00 12:00 16:00 20:00
Temperature (°C)
Heang Setpoint 29
Fig. 6. BRM performance verification for 6-10 February 2013 (winter) and 7-8 August 2013 (summer).
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Table 6 Fixed setpoint annual energy comparison (kWh).
HVAC Electricity Thermal Energy Total HVAC Energy
RBC
Persistence Fixed Setpoint MPC
Persistence Fixed Setpoint MPC %
Persistence Deadband Setpoint MPC
Persistence Deadband Setpoint MPC %
1349614 673597 2023211
1345149 603516 1948665
100% 90% 96%
1208014 633170 1841184
90% 94% 91%
RBC HVAC Electricity
RBC Thermal Energy
Persistence Fixed Setpoint MPC HVAC Electricity
Persistence Fixed Setpoint MPC Thermal Energy
Persistence Deadband Setpoint MPC HVAC Electricity
Persistence Deadband Setpoint MPC Thermal Energy
350000
Energy(kWh)
300000 250000 200000 150000 100000 50000 0 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Fig. 7. Monthly fixed setpoint HVAC energy usage comparison.
Winter
Temperature (°C)
RBC 25
Persistence Fixed Setpoint MPC
Persistence Deadband Setpoint MPC
23 21 19 17 08:45
08:30
08:15
08:00
07:45
07:30
07:15
07:00
06:45
06:30
06:15
06:00
05:45
05:30
05:00
05:15
15
Fig. 8. Average winter morning temperature (solid line) with maximum and minimums for the season (dashed lines).
Summer Persistence fixed setpoints MPC
RBC Persistence deadband setpoints MPC
25 23
Persistence
Persistence DB
Frequency
Temperature (°C)
RBC 27
21
21
19
21.5
22
22.5 23 23.5 Temperature (°C)
24
24.5
25
17 08:45
08:30
08:00
08:15
07:45
07:30
07:15
07:00
06:45
06:30
06:15
06:00
05:45
05:30
05:15
05:00
15
Fig. 9. Average summer morning temperature (solid line) with maximum and minimums for the season (dashed lines).
A comparison of occupancy comfort during occupied periods (08:00–18:00, Monday–Friday) can be found in Fig. 10, which shows that RBC manages to maintain 23 ◦ C more consistently than both fixed setpoint and deadband setpoint persistence MPC techniques. The deadband setpoint persistence technique performs
Fig. 10. Fixed setpoints occupancy comfort comparison (08:00 – 18:00, Monday – Friday).
better than fixed setpoint persistence in that it has a larger number of values in the bins for 23 ◦ C and 23.5 ◦ C that correspond to the defined thermal comfort metric. Fig. 11 illustrates the difference the individual 32 zone temperatures for a winter day and summer day for RBC and persistence deadband setpoint MPC control strategies. The figures highlight the challenge of attempting to optimize for the entire building at once, as some zones heat up faster than others, and illustrates the
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Fig. 11. All zone temperatures (colors) shown with setpoints (red) for winter (left) and summer (right) periods.
potential for savings by moving to a zone level based optimization strategy. The trends also help illustrate why the persistence deadband MPC case has constantly fluctuating setpoints, as the control is based on the average of all zone temperatures, and this fluctuates a small amount with time. 6. Conclusions and recommendations The results of the MPC work outlined in this paper show that a reduction in building HVAC energy use of 4% can be achieved with fixed setpoint pairs, but at the expense of fluctuating temperatures during the daytime hours. The 4% savings are comprised of a 10% reduction in thermal energy and no reduction in HVAC electricity. A second comparison is made between the original RBC (fixed at 23 ◦ C) and the persistence MPC with a setpoint deadband. This is the likely scenario for implementing MPC technology on existing buildings. In this case, the MPC gives a 9% total HVAC energy reduction, with a 6% reduction in thermal energy and 10% HVAC electricity reduction. A key trend from this is that while total savings increased, the thermal energy actually had smaller savings than when all scenarios used fixed pairs. This occurs because the strict conditions of using fixed temperature setpoint pairs adds energy to the heat pump loop from zones that would heat beyond the setpoint, whereas with a dead band the zone is allowed to freely rise in temperature until the cooling value is reached. A simplified thermal comfort evaluation was conducted to compare the results of RBC to the persistence fixed setpoint MPC and persistence deadband MPC. RBC had the best occupancy comfort when considering the strict 23 ◦ C target, followed by persistence deadband MPC, and then persistence fixed setpoint MPC for the occupied period (08:00–18:00, Monday–Friday). The initial savings indicated by the work are promising due to the use a persistence based forecast methodology, a small solution space and limited forecast horizon. It also employed a rather simple thermal comfort of precisely 23 ◦ C. Due to these limitations, there exists areas for improving the MPC results:
• Expand the objective function: this includes cost savings (energy and demand charges, time of use energy pricing [13]), greenhouse gas emission reduction (e.g., electricity vs thermal savings), and occupancy comfort (e.g., Predicted Mean Vote of Percentage of People Dissatisfied [25]). • Expand the solution space: optimize on each zone instead of the average zone; provide additional setpoint solutions (more than 3); consider additional periods such as overnight free-cooling. • Enhance the forecast: use of high-accuracy site-specific forecasting will advantage MPC compared with persistence forecast methods; it also enhances confidence in using a longer forecast horizon. • Faster optimization techniques: brute force is slow but accurate; genetic algorithms, particle swarms, and others [7,26] should allow for an expanded forecast horizon and solution palette with a reasonable increase in computing power and time. Acknowledgements The authors gratefully acknowledge the assistance and support provided by Green Power Labs Inc. throughout this research. In addition, the authors appreciate major funding support provided by the Atlantic Canadian Opportunities Agency supporting innovative economic growth in Canada’s Atlantic Provinces. Additional funding was provided by MITACS (IT03561). References [1] Natural Resources Canada, Energy Use Data Handbook 1990–2010, 2013. [2] Whelton, M. G. Cost performance of energy efficiency measures in residential retrofit projects. IGLC 2012—20th Conference of the International Group for Lean Construction. (2012). [3] S. Wang, Z. Ma, Supervisory and optimal control of building HVAC systems: a review, HVAC R Res. 14 (1) (2008) 3–32. [4] Erickson, L. Varick, Occupancy modeling and prediction for building energy management, ACM Trans. Sensor Netw. 10 (3) (2014), 42:1–42:28. [5] Guiles Jr., G. Ellis, Building professional accreditation, construction quality control and better buildings, ASHRAE Transactions 117 (1) (2011) 170–177.
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[6] S.C. Bengea, A.D. Kelman, F. Borrelli, R. Taylor, S. Narayanan, Implementation of model predictive control for an HVAC system in a mid-size commercial building, HVAC and R Research 20 (1) (2014) 121–135, http://dx.doi.org/10. 1080/10789669.2013.834781. [7] C.D. Corbin, G.P. Henze, P. May-Ostendorp, A model predictive control optimization environment for real-time commercial building application, Journal of Building Performance Simulation 6 (3) (2013) 159–174, http://dx. doi.org/10.1080/19401493.2011.648343. [8] A. Afram, F. Janabi-Sharifi, Theory and applications of HVAC control systems A review of model predictive control (MPC), Building and Environment 72 (2014), http://dx.doi.org/10.1016/j.buildenv.2013.11.016, 343e355-343e355. [9] T. Hilliard, M. Kavgic, L. Swan, Model predictive control for buildings: Trends and opportunities, Advances in Building Energy Research. (2015), Accepted June 8, 2015 (TAER-2014-0071.R1). [10] Nova Scotia Community College. Waterfront Campus. http://www.nscc.ca/ explorenscc/campuses/waterfront/(accessed 04.04.2014). [11] Building Controls Virtual Test Bed. Lawrence Berkeley National Laboratory. https://simulationresearch.lbl.gov/bcvtb (accessed 06.08.2015). [12] R. The R Project for Statistical Computing. http://www.r-project.org/(accessed 05.04.14). [13] M. Avci, M. Erkoc, A. Rahmani, S. Asfour, Model predictive HVAC load control in buildings using real-time electricity pricing, Energy and Buildings 60 (2013) 199–209, http://dx.doi.org/10.1016/j.enbuild.2013.01.008. [14] P. Morosan, R. Bourdais, D. Dumur, J. Buisson, Building temperature regulation using a distributed model predictive control, Energy and Buildings 42 (9) (2010) 1445–1452, http://dx.doi.org/10.1016/j.enbuild.2010. 03.014. [15] J. Zhao, K.P. Lam, B.E. Ydstie, EnergyPlus model based predictive control (EPMPC) by using Matlab/Simulink and MLE+, in: 13th Conference of International Building Performance Simulation Association, Chambéry, France, 2013, pp. 2466–2473, August 26-28, 2013. [16] B. Lehmann, D. Gyalistras, M. Gwerder, K. Wirth, S. Carl, Intermediate complexity model for model predictive control of integrated room automation, Energy and Buildings 58 (2013) 250–262, http://dx.doi.org/10. 1016/j.enbuild.2012.12.007.
[17] J. Siroky, F. Oldewurtel, J. Cigler, S. Privara, Experimental analysis of model predictive control for an energy efficient building heating system, Applied Energy 88 (9) (2011) 3079–3087, http://dx.doi.org/10.1016/j.apenergy.2011. 03.009. [18] Neto, H. Alberto, Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, Energy and buildings 40 (12) (2008) 2169–2176. [19] Tao. Lu, A hybrid numerical-neural-network model for building simulation: A case study for the simulation of unheated and uncooled indoor temperature, Energy and buildings 86 (2015) 723–734. [20] S. Privara, J. Siroky, L. Ferkl, J. Cigler, Model predictive control of a building heating system: The first experience, Energy and Buildings 43 (2–3) (2011) 564–572, http://dx.doi.org/10.1016/j.enbuild.2010.10.022. [21] S.R. West, J.K. Ward, J. Wall, Trial results from a model predictive control and optimisation system for commercial building HVAC, Energy and Buildings 72 (2014) 271–279, http://dx.doi.org/10.1016/j.enbuild.2013.12.037. [22] A. Liaw, M. Wiener, Classification and Regression by randomForest, R News 2 (3) (2002) 18–22. [23] T.L. McKinley, Identification of building model parameters and loads using on-site data logs, Governing 10 (2008) 3. [24] ASHRAE Guideline 14-2002 – Measurement of Energy and Demand Savings, ASHRAE. [25] B. Coffey, Approximating model predictive control with existing building simulation tools and offline optimization, Journal of Building Performance Simulation 6 (3) (2013) 220–235, http://dx.doi.org/10.1080/19401493.2012. 737834. [26] P. May-Ostendorp, G.P. Henze, C.D. Corbin, B. Rajagopalan, C. Felsmann, Model-predictive control of mixed-mode buildings with rule extraction, Building and Environment 46 (2) (2011) 428–437, http://dx.doi.org/10.1016/j. buildenv.2010.08.004. [27] US DOE. “EnergyPlus Energy Simulation Software”. http://apps1.eere. energy.gov/buildings/energyplus/(accessed 04.06.2014). [28] G. Zheng, Y. Jing, Thermodynamics performance study on water source heat pump in variant operating condition, Energy and Environment 20 (4) (2009) 517–532.