Development of a model predictive control framework through real-time building energy management system data

Applied Energy 155 (2015) 1–13

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Development of a model predictive control framework through real-time building energy management system data Younghoon Kwak a, Jung-Ho Huh a,⇑, Cheolyong Jang b a b

Department of Architectural Engineering, University of Seoul, Seoul, South Korea Energy Efficiency Research Division, Energy Saving Laboratory, Korea Institute of Energy Research, Daejeon, South Korea

h i g h l i g h t s  A model predictive control framework was established for control.  Real-time co-simulation with external data input was implemented using Building Controls Virtual Test Bed.  Comparing the real-time predicted and measured energy consumption values, it was within the allowable statistical error range.

a r t i c l e

i n f o

Article history: Received 25 October 2014 Received in revised form 27 May 2015 Accepted 29 May 2015

Keywords: Real-time Building energy management system data Model predictive control (MPC) Framework Building Controls Virtual Test Bed (BCVTB)

a b s t r a c t Over the past several years, studies have been conducted on the model predictive control (MPC), which has analyzed the amount of savings through model-based predictive control. As its result is dependent on the precision and accuracy of the model, minimizing the errors due to arbitrary events caused by the occupants as well as uncertain data input is important. In this study, to address these errors, real-time building energy simulation was conducted in accordance with which energy consumption could be predicted and a MPC framework was established for control. To use the building energy management system data, co-simulation was implemented based on the Building Controls Virtual Test Bed (BCVTB) as it allows for a real-time simulation based on the external data input. The real-time predicted and measured energy consumption values were compared to the statistical indices such as the hourly Mean Bias Error and Coefficient Variation of the Root-Mean-Squared Error with acceptable values of 0.7% and 19.1%, respectively. As a case study, an enthalpy control algorithm with the real-time monitoring data was successfully implemented producing the damper position of the air handling unit. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Energy consumption in buildings can be improved by various measures such as pursuing simple behavioral improvements or using highly efficient LED lighting. Among these measures, many researchers implement model predictive control (MPC), which can improve system performance through model-based predictive control. According to Mahdavi [1], the building performance simulation (BPS) has traditionally conducted modeling in the design stage, but now, simulation can be used in the operation stage by coupling a control algorithm for modeling using a simple scenario. He also pointed out that the simulation could be used in determining the effective methods in the decision-making process. MPC requires a model that uses a building energy simulation tool, which has been developed into the current state after a ⇑ Corresponding author. Tel.: +82 2 6490 2757; fax: +82 2 6490 2749. E-mail address: [email protected] (J.-H. Huh). http://dx.doi.org/10.1016/j.apenergy.2015.05.096 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.

40-year-long development process. The development of the simulation tool has passed the fourth generation [2]. The intention is only to provide users with an indication of performance: a 1st generation program is consequently easy to apply but difficult to interpret since the user is required to appreciate its limitations and make appropriate allowances. In the mid-1970s, 2nd generation programs began to emerge. These stressed the temporal aspect of the problem, particularly with respect to the long time constant elements such as multi-layered constructions. The underlying calculation methods remained analytical and piece meal: time or frequency domain response factors were used to model the dynamic response of constructional elements, while HVAC system modeling was confined to the steady state. With the advent of more powerful personal computing, 3rd generation programs began to emerged as a viable prospect in the mid-1980s. These presumed that only the space and time dimensions were independent variables; and all other system parameters were dependent so that no single energy transfer process could be solved in isolation. This

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signalled the beginning of integrated modeling whereby the thermal, visual, and acoustic aspects of the performance were considered together. In the mid-1990s domain integration work continued apace but with the addition of program interoperability that essentially was a data modeling issue. Also, and in response to the growing uptake by practitioners, new development commenced that was concerned with knowledge-based user interfaces, application quality control and user training. The evolution of design tools, from traditional manual methods to contemporary simulators, is summarized in Table 1 [3]. Such simulation tools have widely been implemented, and for the last several decades, they have allowed for various advancements including acceleration in the design process, increasing the efficiency, and allowing the comparison of a wide range of design variables leading to optimal designs for building performance evaluation [4]. Building controls basically seeks to expand the scope of control and determine the control behavior for the purpose of providing thermal comfort to the occupants and minimizing the energy consumption. Among the existing building control methods is feedback control, which measures the control results, compares the set variables, and gives feedback on the control signal to reduce the errors. The control signal generated at present, however, may not correspond to the conditions in the next time slot, which may lengthen the time needed to reduce errors. Furthermore, the control signal may not reach the set variable in the case of disturbance. In contrast, if the control signal corresponds to the current time and the possible disturbance can be predicted and controlled (feed forward control) then the set variables can be reached quickly and accurately. MPC is ideal for solving this problem. The basic concept of MPC is to predict the future conditions of a system and calculate the control behaviors based on such a prediction [5]. Some simulation tools such as TRNSYS and EnergyPlus allow one to conduct building-control-related research more easily than other tools, if the specific functionalities of these tools are used. However, such simulation tools have limitations, including simplified modeling, uncertain data input, occupant-generated events, and a simplified schedule, etc., which may hinder ideal MPC research. Moreover, MPC research on buildings has been implemented in various research on saving loads, electricity-market-related control, control based on building thermal capacity, HVAC parameter control, optimal control, etc. These studies can generally be categorized into investigations connecting the optimization method and probabilistic method to determine the control behaviors or to improve the control performance. The following are the representative studies that incorporated the use of the optimization method. A typical HVAC was designed to meet the required conditions, regardless of the energy costs. There is a growing interest in maintaining comfort conditions and reducing the HVAC energy cost by using efficient equipments, new approaches to HVAC design, and supervisory control.

Wemhoff et al. [6] calibrated the existing input parameters in various HVAC devices. Wallace et al. [7] conducted an MPC study by focusing on the control of the vapor compressor cycle in a cooling system, particularly on the optimal control of the vapor compressor cycle with the compressor speed and expansion valve position as control variables. Ma et al. [8] conducted an MPC study for the cold-water tank system, designing the chiller, cooling tower and the cold-water tank for MPC control and determining the amount of cold water for the optimal operation of the cooling system by predicting the building loads and weather conditions. The process was conducted in real-time using MATLAB to find the optimal solution in 20 min on average from a one-hour timestep. Coffey et al. [9] conducted predictive control by adding the revised generic algorithm (GA) to GenOpt and a high-standard integrated supervisory control strategy. They also developed an optimal MPC by coupling a low-energy-consumption system and demand response (DR) control and conducted a case study of DR with increasing indoor office temperature. Kummert et al. [10,11] studied the optimal heating control that can minimize the occupants’ dissatisfaction (Fanger’s PPD) because of overcooling in the morning and overheating in the afternoon in a passive solar building, and minimizing energy consumption. The MPC study was performed using MATLAB and TRNSYS. Henze et al. [12] applied the MPC method to use the ice storage and thermal mass of a building. Using MATLAB, they used the controller that corresponded to 24- and one-hour timesteps and conducted optimal control as well as the building modeling of TRNSYS. The following are representative studies that combined the probability methods. Prívara et al. [13] conducted two case studies. First, they combined EnergyPlus and a statistical method called 4SID algorithm and applied it to a large office building. Second, they applied probabilistic methods such as TRNSYS and MRI (MPC relevant identification), Deterministic semi-physical modeling (DSPM), and Probabilistic semi-physical modeling (PSPM) to an artificial model. These two methods produced appropriate results for control by considering the probability of the predictive scope, but in the case of large buildings, large data, and complicated systems, calculation by these methods was difficult. Therefore, these methods were recommended for buildings with simple structures. Oldewurtel et al. [5] analyzed MPC and weather predictions with a probabilistic method to consider a plan that provides thermal comfort to the occupants in an integrated indoor automation system and increases the energy efficiency. In this way, they aimed to expand the control scope by using the thermal mass possessed by a building. MPC studies should be preceded by an accurate prediction, whereas, past studies focused on control. Thus, recent studies have attempted to increase the predictive rate by combining online models, which have not been ideal because of their long processing time for simulation, defects in the optimization because of formulas, and difficulty in the re-initiation of the model against specific timesteps [12]. Furthermore, such methods can change because

Table 1 Types of co-simulation and related researches [3]. Generation

Characteristics

Consequences

1

Handbook oriented simplified and piecemeal familiar to practitioners

2

Building dynamics stressed less simplified, still piecemeal based on standard theories Field problem approach shift to numerical methods integrated modeling stressed graphical user interface partial interoperability enabled

Easy to use, difficult to translate to real world, non-integrative, application limited, deficiencies hidden ;

3

4 and beyond

Good match with reality intelligent knowledge-based fully integrated network compatible/interoperable

Increasing integrity vis-à-vis the real world ; Deficiencies overt, easy to use and interpret, predictive and multi-variate, ubiquitous and accessible

Y. Kwak et al. / Applied Energy 155 (2015) 1–13

of the unique limitation of the simulation and the inaccuracy of weather forecasting. Therefore, an MPC study that uses building energy simulation requires the development of a precise online model and a real-time building energy simulation tool, allowing one to reflect the current conditions onto the input model of the simulation for increasing the reliability of the simulation results and can be used as an alternative. In other words, by coupling feed forward control, which predicts and controls the occurrence of any errors against the actual control in advance to the past MPC studies, then reasonable control results can be produced. MPC studies can produce different results based on the precision and accuracy of the model used; thus, minimizing the input of uncertain data and errors because of the arbitrary events caused by the occupants is necessary [14]. Therefore, this study aimed to minimize the errors by performing real-time building energy simulation based on the weather data, lighting and plug energy consumptions, among the BEMS data. In particular, the error rate in the hourly or minute timestep tends to increase in the simulation, necessitating the need of real-time simulation. The study used the weather data, such as measured weather elements, to complement the input of uncertain data while reflecting the current conditions and also used the energy consumption data, such as lighting and plug energy consumptions, closely related to the behavioral pattern of the occupants to complement the arbitrary events generated by the occupants. In this study, to use these data, co-simulation was conducted using the Building Controls Virtual Test Bed (BCVTB), allowing for the real-time simulation based on the input of external data. This improves the prediction rate; thus, the control results from the control algorithm were more reliable than those of past studies. In other words, as shown in Fig. 1, the MPC framework aimed to develop in this study predicts the energy and calculates the control results, such as energy savings and control variables, using the BEMS-data-based real-time building energy simulation. Later, the predicted energy consumption and control results are sent to the energy monitoring control system (EMCS) of the target building and offered to the building management operator. The control variables are connected to the BAS for the functioning of the building facility system, and the predicted energy consumption is compared to the measured energy consumption via in-home display (IHD). At this point, EMCS monitors the BEMS data; building automation system (BAS) monitors to the automation system installed in the building; and IHD refers to the monitoring screen.

2. Preliminary studies 2.1. BCVTB BCVTB, developed by Lawrence Berkeley National Laboratory (LBNL), is software that connects the simulation program and hardware for co-simulation. That is, it is a middleware that functions as a bridge between heterological systems. The data between building energy simulation and other software can be exchanged using the BCVTB. For example, EnergyPlus can perform building modeling; Modelica [15] can model the HVAC control system; and the results can be combined for simulation purposes. The BCVTB, a Java-[16]-based software framework designed by LBNL, is based on the Ptolemy II [17] software, which was developed to perform the heterological simulation. Thus, specialists can expand the functions of each element of software by coupling these heterological programs. Wetter and Haves [18] explained the development process and structure of BCVTB and discussed the role of the middleware by comparison when coupling it with various clients. As a case study, they conducted natural ventilation control simulation

3

by coupling EnergyPlus and MATLAB/Simulink on BCVTB. Furthermore, Wetter [19] described the coupling of EnergyPlus, Modelica, MATLAB, and Simulink on BCVTB. As a case study, he performed natural ventilation and shading control by coupling EnergyPlus and MATLAB/Simulink. 2.2. Co-simulation With the advancement of computer and IT technology, BEMS has also advanced, and accordingly, the building energy simulation tools have been developed in various ways [20]. The previous building energy simulation tools were often used individually, however, recently, two or more simulations and software tools have been connected to perform the co-simulation, and the corresponding research has been active. Co-simulation aims to use the advantages, and overcome and complement the disadvantages of each simulation tool or software by coupling two or more simulations and software tools. In particular, building simulation tools are not sufficient for connecting building control technology for the fast execution of complex calculations, and as such, co-simulation has emerged as an alternative. Trcˇka et al. [21] studied simulation coupling strategies and data exchange. Co-simulation connects two or more simulators to solve differential equations. By coupling these equations, specific cases of simulation scenarios, where data can be exchanged are shown. The data variables can acquire stability, convergence, accuracy, and efficiency by co-simulation. As for the simulation coupling strategies for data variables, strong and loose couplings have been discussed. Co-simulation literature was researched extensively because of the previous and latest development of BCVTB (Table 2). In the studies on coupling EnergyPlus and MATLAB, Kim and Park [24] pointed out various shortcomings by describing the physical phenomena on the double skin facade (DSF). To overcome these problems, they coupled the DSF developed model under MATLAB to the EnergyPlus model. Sagerschnig et al. [25] studied the possibility of advanced control through the performance evaluation for energy consumption, thermal comfort, and peak electricity load, as well as simulation. In this study, the temperature set at the plant was determined by MATLAB, and the use of electric equipment and EnergyPlus coupled to BCVTB set the equipment-operating schedule and internal loads. Therefore, co-simulation can overcome the problems in building energy simulation tools. Moreover, Beausoleil-Morrison et al. [35] studied on co-simulation for ESP-r and TRNSYS, even though it was not on BCVTB. Discerning the framework used in this study from the aforementioned co-simulation was necessary. In the study by BeausoleilMorrison et al., the exchange of data variables occurred between coupled heterological simulators, whereas in this study, the data variables from the real-time data and EMCS were not limited to the study on co-simulation entered via BCVTB, but were exchanged sequentially between MATLAB, EnergyPlus, and other types of software. Fig. 2 shows the order based on the research process. 2.3. Real-time predictive control In this study, a predictive control was used for HVAC system of the building to change indoor thermal condition. Unlike the other control environment of the dynamic system in general, a certain amount of time lag exits when operating the whole building. For example, stepping on the brakes allows cars to stop immediately, while activating cooling system of the building does not allow indoor to reach the set-point temperature immediately. Once the cooling system starts to operate due to the high indoor temperature, the effect reveals only after sometime (hereinafter, ‘after 15 minutes’). On the contrary, continuous operation of the system

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Framework Real Building

Virtual Building

External Interface

Weather Energy BAS Database EM(C)S

IHD

timestep

Predicted

Savings

Control variables

Fig. 1. Concept and research scope of the MPC framework.

Table 2 Types of co-simulation and related researches. Types of co-simulation

Research

EnergyPlus + MATLAB

Wallace et al. [7] Prívara et al. [13] Wrobel et al. [22] Ma et al. [23] Kim et al. [24] Sagerschnig et al. [25] Pang et al. [26] Zhang et al. [27] Kwak et al. [28] Nembrini et al. [29] Moon et al. [30] Nouidui et al. [31] Nouidui et al. [32] Hafner et al. [33] Yang et al. [34]

EnergyPlus + EMCS EnergyPlus + CFD EnergyPlus + Real-time (www) EnergyPlus + Radiance EnergyPlus + BACnet Modelica + FMU Coupling two or more tools

at same temperature will not maintain the condition and make the indoor colder, which would require another set-point temperature and 15 min to adjust into thermal comfort. Due to this specific reason, a predictive control is required for the building to reduce time lag and adopt current condition.

Therefore, this study developed a framework using different control method to predict the situation in the building 15 min ahead and implement control system reflecting the predicted value. The framework reflects the prediction result and provides control information in advance of the current time (predicted energy consumption, controlled energy consumption, control variables). In other words, if the potential future at 15 min is known and controlled in advance, and if the corresponding control of every 15 min is performed in advance, the appropriate control after 15 min can be reflected. This can be considered as a real-time control suitable to circumstances; real-time control of HVAC system would include the possible time lag of the building thus requires predicted data to maintain thermal comfort. The accuracy of time lag in real time would require numerous factors to consider according to the building environment which makes difficult to study and analyze without any presumption. A 15-min time interval has been accepted as a general practices for the whole building energy management and the simulation of the study just followed this. A similar study was previously performed by Pang et al. [26]. Every 5 min data was read through BACnet and the energy simulation was performed every 15 min to realize a framework for a real-time whole building performance assessment.

EM(C)S

Control algorithms

1. Real-time weather data & Lighting, Plug

7. Building energy prediction

8. Calculate control values

BCVTB (Ptolemy II) 2. Weather elements & Lighting, Plug

3. Month, Day (today), Day of week (today) & (timestep+ 1)

4. Begin month, day End month, day, Day of week

5. Modify .idf file

MATLAB

6. Revised model simulate

EnergyPlus Fig. 2. Research process.

9. Final Simulate and analysis

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Y. Kwak et al. / Applied Energy 155 (2015) 1–13

(a)

(b)

M/B

Ice Storage

EA

C/T OA

C/C Chiller

H/C Zone

Zone Boiler

Fig. 3. Building modeling (a) modeling; (b) schematic system diagram [36].

3.2. Baseline model

Table 3 Internal loads [28]. Parameters

Values

Lighting

10.9 W/m2

Plug (electric equipment)

15 W/m2

Occupants

1st floor, 53 people 2nd floor, 29 people 3rd floor, 45 people 4th floor, 55 people 5th floor, 26 people

3. MPC framework 3.1. Overview In this study, a BEMS-data-based real-time building energy simulation was developed as the framework, producing control results by predicting the energy consumption and applying the building control algorithm. At this point, building control (meaning MPC as the control), was performed after the prediction of energy consumption through modeling. The BEMS data included weather elements such as outdoor dry-bulb temperature, relative humidity, wind direction, and wind speed, and energy consumption data such as lighting and plug energy consumption. The BEMS data were entered in real-time at each timestep (15 min). The predicted energy consumption was compared and analyzed with the measured energy consumption of the target building. Herein, the applied control method was the enthalpy control, which was applicable to the cooling system of a generic office building. Moreover, the framework was based on the following assumptions.  The weather elements at the current timestep were identical to those of the next timestep.  The lighting and plug energy consumption values at the current timestep were identical to those of the next timestep.  The cooling system operation schedule was set in advance. In this framework, the prediction of energy consumption was performed at each timestep. Although there were some changes in the weather elements at short timestep intervals, such changes were negligible. Moreover, the lighting and plug energy consumption values did not change drastically for each timestep. Furthermore, office or large buildings are run based on individual schedules. For example, the cooling system runs from 8 AM to 5 PM if the indoor temperature is >26 °C, and during the energy saving time between 2 and 4 PM, the cooling system stops operating. Therefore, the abovementioned assumptions are reasonable.

In this study, an office building (total floor area: 39,973 m2) in South Korea was selected as the baseline model. The model was constructed using OpenStudio and EnergyPlus. Its zoning was based on air-handling units (AHUs) and subcategorized into interior and perimeter zones. The thermal performance and internal loads of the envelopment of the target building were based on the design drawings and field surveys. Furthermore, the occupant schedule was set based on the actual usage time, and the lighting and plug schedules were entered in real-time by the framework, and their values changed. A DSF was installed on the southern part of the target building, and an ice storage facility was installed in the basement. The HVAC system consisted of four variable air volumes (VAVs). Fig. 3 [36] shows the completed modeling (a) and the schematic system diagram (b). 3.2.1. HVAC systems For the cooling system, two types of HVAC systems are used. The first type is the VAV system, which is connected with two central screw chillers (total 60 RT). The two screw chillers manage the day cooling loads together with ice storage (480 RT), and at night, they charge the ice storage using nighttime electricity sources. Four air handling units (AHUs) are used. The 1st AHU is responsible for the south and west sides in the 3rd to 5th floors, the 2nd AHU is responsible for the rest of the east side and the basement in the same floors, the 3rd AHU is responsible for the 1st and 2nd floors, and the 4th AHU is responsible for cooling the conference room on the 2nd floor. There are stairs and toilets in the northern side, known as a core zone. The second type is the EHP (Electric Heat Pump) system, which is placed in the conference room on the 1st floor. The heating system is operated by two steam boilers (total 1300 kg/h) for steam and hot water. The steam created in the boiler is used in the heating coils of the AHUs and convectors located in the perimeter zone, after being converted to hot water through heat exchange [28]. 3.2.2. Calibration of the internal loads schedule In this study, energy predictions were predicted according to changes in real-time weather conditions, so calibration was not conducted according to monthly or hourly energy consumption. However, the internal loads were calibrated because the schedule and density of the internal loads have a huge effect on the building load profile. The initial simulation model was created based on field survey data. The actual values of internal loads composed of lighting, plug (electric equipment), and occupants were obtained by implementing an energy audit, as summarized in Table 3 [28].

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3.2.3. Initial baseline model To create the initial baseline model, the monthly errors of the simulated energy consumption was calculated using the actually measured electricity energy consumption from the chillers, cooling tower, EHPs, fans, pumps, lightings, and plugs of the target buildings from July to December. The error calculation was performed using Mean Bias Error (MBE) and Cv(RMSE). The MBE and Cv(RMSE) of the whole building energy consumption were 1.75% and 4.84%, respectively, satisfying the error criteria [37,38].

1.00 0.90

Initial Calibration

0.80

Fraction

0.70 0.60 0.50 0.40 0.30

3.3. Implementation of the framework

0.20 0.10 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

(a) Lighting 1.00 0.90

Initial Calibration

The framework was implemented in a total of four stages as in the following:  Step 0: Preparation  Step 1: Copying BEMS data from EMCS  Step 2: Baseline model (idf) revision and copying the BEMS data at the current timestep  Step 3: Energy consumption prediction and control

0.80

Fraction

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

(b) Plug

In steps 0–3, BCVTB, EMCS, MATLAB and EnergyPlus were coupled together to exchange data and generate the final outcomes. Step 0 was EMCS, which needed to be prepared in advance. Step 1 used the weather element and energy consumption data from EMCS. The entered values were daily accumulated variables. Step 2 was divided into two parts by role. First, the date of the next-day was calculated using MATLAB, and the simulation date within the baseline model (idf) of EnergyPlus was revised. Second, from the daily accumulated value, the value at the most recent timestep was assumed to be the value of the next timestep and was copied. Finally, in step 3, the simulation date was revised, and the weather element and energy consumption values (the second part of step 2) were entered to predict the energy consumption of the next timestep and calculated the control results (energy consumption and control variables).

1.00 0.90

Initial Calibration

3.3.1. Step 0: Preparation Step 0 was the preparation stage for the implementation of the framework. First, the target building was equipped with EMCS, which was the BEMS. Advanced metering infrastructure (AMI), and the EMCS of the target building, was installed in the target building. Fig. 5 shows the AMI of the target building. Moreover, the EMCS of the target building logs the weather elements from the weather station installed at the rooftop, as shown in Fig. 6. Table 4 shows the specifications of the weather station measurement equipment.

0.80

Fraction

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

(c) Occupant Fig. 4. Calibration results of the internal load schedule [28].

The schedule was calibrated according to the hourly mean energy consumption data. Fig. 4 [28] shows the calibration results of the internal load schedule. The calibrated lighting load schedule was quite different from the initial model because the installed lighting was turned off completely during the daytime. There were slight differences in the calibrated plug load schedule in the afternoon. However, the calibrated occupant schedule was very similar to the initial model [28].

3.3.2. Step 1: Copying the BEMS data from EMCS The BEMS data logged from EMCS were logged onto the database of the server, which was set to enter the weather elements and energy consumption data used for this study using BCVTB. 3.3.3. Step 2: Revising the baseline model (idf) and copying the BEMS data of the current timestep 3.3.3.1. Revising the baseline model. As the framework needed to be simulated in real-time, the baseline model had to be revised to a new date every day and simulated. Then, in this study, BCVTB was designed by revising the simulation execution date based on the following stages: (1) calculating the day and day of week; (2) copying the simulation of the baseline model up to the front of the date;

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User

Fig. 5. AMI installed on the target building.

Temperature & Humidity sensor Wind speed sensor

the next timestep (ti+1) was based on the addition of the BEMS data from the initial timestep (t0) to the current timestep (ti) and to the BEMS data of the current timestep, as shown in Eqs. (1) and (2). Later, energy simulation was conducted (refer to Section 3.3.4.1).

ðDBT; RH; WD; WSÞt0 tiþ1 ¼ ðDBT; RH; WD; WSÞt0 ti þ ðDBT; RH; WD; WSÞti ðEl ; Ep Þt0 tiþ1 ¼ ðEl ; Ep Þt0 ti þ ðEl ; Ep Þti

Wind direction sensor

Fig. 6. Weather station installed on the target building.

(3) input of the month/day when the simulation was begun and ended; (4) input of the day of the week when the simulation was conducted; and (5) copying the end of the baseline model simulation date.

3.3.3.2. Copying the BEMS data at the current timestep. The BEMS data of the current timestep was mentioned to be identical to that of the next timestep. Such an assumption was made to execute the energy prediction of the next timestep. The energy prediction of Table 4 Specifications of the weather station measurement equipment. Measuring equipment

Range of measurement

Measurement unit

Temperature Humidity Wind direction Wind speed

40 to 123.8 °C 0–100% 0–359° 1–70 m/s

0.1 °C 0.1% 1° 0.1 m/s

ð1Þ ð2Þ

where DBT is the dry-bulb temperature [°C], RH is the relative humidity [%], WD is the wind direction [°], WS is the wind speed [m/s], El is the lighting energy consumption [kW h], Ep is the plug energy consumption [kW h], t0 is the first timestep of the day, ti is the current timestep of the day, and ti+1 is the next timestep of the day. 3.3.4. Step 3: Energy consumption prediction and predictive control 3.3.4.1. Prediction. Table 5 shows the energy consumption prediction process based on the hypothesis stated in Section 3.1. (1) 1st timestep (00 min) Once the weather elements, and lighting and plug energy consumption are measured at 00 min, the framework predicts the whole building energy consumption at 15 min. At that point, the weather element, and lighting and plug energy consumption at 15 min are assumed to be the same as at 00 min, then copied to predict the whole building energy consumption at 15 min. These copied data are input into a simulation of a framework (EnergyPlus) while the whole building energy consumption at 15 min is predicted as the output of simulation. At this point, the reason why the whole building energy consumption for 15 min long after assuming that the value of 00 min is the same as that of 15 min is that the weather data file (epw) and input file (idf) for stimulation are required for the distinct characteristics of a simulation (EnergyPlus). In addition, the historical lighting and plug

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Y. Kwak et al. / Applied Energy 155 (2015) 1–13 Table 5 An example of the energy consumption prediction process.

Iteration

Timestep (15 min.)

00 min.

15 min.

30 min.

45 min.

30 min.

45 min.

Copy data

1st

Prediction

00 min.

15 min.

1st Real-time BEMS data Copy data

Update

Prediction

2nd

Integrate all data just before the last timestep

00 min.

15 min.

30 min.

45 min.

1st

2nd Real-time BEMS data Update

3rd

Copy data Prediction

Integrate all data just before the last timestep

Real-time BEMS data (Weather elements, lighting and plug energy consumption) Whole building energy consumption Predicted whole building energy consumption

schedules are varied in a real-time manner to apply them in a simulation (historical lighting and plug schedules are the initial input value and can be varied anytime in a real-time manner that may not be suitable to a prediction by timestep.) (2) 2nd timestep (15 min) When the time reaches 15 min, the measured weather element, and lighting and plug energy consumption are used in the

framework to predict the whole building energy consumption for 30 min. At this point, weather element, and lighting and plug energy consumption were assumed to be the same as the 1st timestep (00 min) are updated to the weather element, and lighting and plug energy consumption predicted at 15 min. At the same time, the whole building energy consumption is updated. After that, similar to the 1st timestep, the weather element, and lighting and plug energy consumption at 30 min is assumed to be the same as at 15 min, then copied to predict the whole building energy

Y. Kwak et al. / Applied Energy 155 (2015) 1–13

9

EnergyPlus

Real-time BEMS data

Weather elements

Lighting energy consumption Plug energy consumption

Fig. 7. Input external data (weather elements and lighting and plug energy consumption) on BCVTB.

consumption at 30 min. These copied data are input into a simulation of a framework (EnergyPlus) while the whole building energy consumption at 15 min is predicted as the output of simulation. At this point, the whole building energy consumption shortly before the timestep (00 min–15 min) are integrated which is further designed to predict the whole building energy consumption at 30 min. This reflects the latest energy consumption to minimize the error occurred at each timestep, rather than accumulating them. (3) 3rd timestep (30 min) If it reaches 30 min, the whole building energy consumption for 45 min is predicted to be similar to the 2nd timestep. The two assumptions need further explanation. The assumption that the weather elements, and lighting and plug energy consumption values of the current timestep (15 min) are identical to those of the next timestep is reasonable because the energy consumption will be substituted for the measured energy consumption of the next timestep, and the substituted energy consumption will be used to predict the energy consumption of the next timestep. In other words, the errors of the predicted energy consumption of each timestep occur only for the corresponding timestep. If the energy consumption is not substituted, the error rate will continue to accumulate. Therefore, if the gap between the timesteps is small, the predicted error rate will decrease; but, if the gap is large, the predicted error rate will increase. Moreover, the lighting and plug energy consumptions are external input data. The energy consumption in itself cannot be input into EnergyPlus as a variable. The energy consumption is the final outcome, and the variable for calculating the energy consumption should be input.

First, the lighting and plug energy consumptions by hour can be calculated using Eqs. (3) and (4), respectively.

El ¼ ql  Sf ;l  Al

ð3Þ

Ep ¼ qp  Sf ;p  Ap

ð4Þ

where ql is the lighting density [W/m2], qp is the plug density [W/m2], Sf,l is the lighting schedule in fraction (0–1), Sf,p is the plug schedule in fraction (0–1), Al is the lighting area [m2], and Ap is the plug area [m2]. In this study, however, lighting and plug energy consumptions (El and Ep) were input as external data, and therefore, for such data to be input into EnergyPlus, the lighting and plug schedules (Sf,l and Sf,p) needed to be revised and applied. When the lighting and plug energy consumptions were input, the lighting and plug schedules were calculated using Eqs. (5) and (6). The calculated lighting and plug schedules (Sf,l and Sf,p) were input using the External Interface, and the schedule of EnergyPlus.

Sf ;l ¼

El  1000 4 ql  Al

ð5Þ

Sf ;p ¼

Ep  1000 4 qp  Ap

ð6Þ

Herein, the lighting and plug densities (ql and qp) and lighting and plug areas (Al and Ap) are constants. Lighting and plug energy consumptions were multiplied by 1000 to convert from kW h to W h and multiplied by four for calculating the timesteps in 15 min (1 h to 15 min). As the lighting and plug energy consumptions values were entered in timesteps, the simulation was revised to schedules accordingly.

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Y. Kwak et al. / Applied Energy 155 (2015) 1–13

(a)

11:15

11:30

(b) 11:45 11:30 11:15

(c)

Fig. 8. Energy consumption prediction: (a) energy consumption at 11:30 predicted at 11:15; (b) energy consumption at 11:45 predicted at 11:30; and (c) whole day energy consumption prediction.

300

Predicted Measured

Energy consumption [kWh]

250 MBE = -0.7% Cv(RMSE) = 19.1%

200

150

100

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour Fig. 9. Hourly comparison of the measured and predicted energy consumption.

After the above process, the BEMS data of EMCS were entered to perform the energy consumption prediction. Fig. 7 shows the BCVTB designed in such a way so that the external BEMS data could be received and simulated using EnergyPlus.

Energy prediction was performed in real-time by timestep. Fig. 8 shows the execution of the energy prediction. Herein, ‘‘Measure’’ is the energy consumption actually measured from a building, and ‘‘Predicted’’ is the predicted energy consumption. One point is a timestep of 15 min. Fig. 8(a) shows the predicted energy consumption at 11:30 from the real-time simulation with the climate elements and lighting and plug energy consumptions at 11:15. The enlarged part of the predicted energy consumption at 11:30 from that at 11:15 was seen, where the energy consumption was measured up to 11:15, and the energy consumption prediction was done at the same time. At 11:30, however, only the predicted energy consumption was plotted. The energy consumption at 11:30 was predicted using the accumulated whole building energy consumption, including the climate elements and lighting and plug energy consumptions up to 11:15. In other words, at each timestep, the energy consumption was predicted, and not only the energy consumption at the corresponding timestep was predicted, but all the circumstances up to the previous timestep were accumulated and reflected. Similarly, Fig. 8(b) shows the energy consumption at 11:45 predicted at 11:30. The energy consumption at 11:30 predicted at 11:15 shows a small error when compared

Y. Kwak et al. / Applied Energy 155 (2015) 1–13

11

Fig. 10. Developed framework based on BCVTB including prediction and control.

to the energy consumption measured at 11:30 (about 2.3 kW h). Fig. 8(c) shows the predicted energy consumption for one day. Herein, the cooling system ran three times (10:00–11:45, 12:45– 14:00, 15:30–17:00) during daylight for one day. At night (22:00–24:00), the chiller ran for ice storage.

Fig. 9 shows the hourly plot of the measured and predicted energy consumptions for the whole day. The error rate was examined by the statistical value. The hourly MBE and Cv(RMSE) were 0.7% and 19.1%, respectively, which were within the acceptable values.

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Y. Kwak et al. / Applied Energy 155 (2015) 1–13

Fig. 11. Results of the application of the enthalpy control algorithm (08/07/2013).

Table 6 Energy consumption of the application of the enthalpy control.

Energy consumption

Measured [kW h]

Predicted [kW h]

Controlled [kW h]

Savings [kW h]

1586

1652

1643

9 (0.5%)

Table 7 Example of the control variables with enthalpy control. Time

00:15 00:30 00:45 01:00 01:15 01:30 01:45 02:00 ... 10:00 10:15 10:30 10:45 11:00 11:15 11:30 11:45 12:00 ...

AHU1

AHU2

adjust the set-point temperature, so that if the predicted value can be input and enthalpy control operates based on it, then the building would be able to reduce the time lag and allow maintaining its thermal comfort. This would depend on accuracy of assumed prediction, and this has been satisfied in the earlier part of the study that the predicted value is significant enough. Therefore, it can be assumed that the control part of the framework based on the prediction would operate to reduce energy consumption, and this is proven by the simulation as well. Fig. 11 shows the use of the enthalpy control on BCVTB. Herein, ‘‘Controlled’’ is the control result after the application of the enthalpy control algorithm, and ‘‘Savings’’ is the amount of savings, which is the difference between ‘‘Predicted’’ and ‘‘Controlled’’. The energy savings for the whole day was about 9 kW h shown in Table 6. The control variables such as the position of the outdoor air damper, exhaust damper, and return air damper on the three AHUs are shown in Table 7. Cumulative damper positions during a day were calculated in real-time. In the developed framework, the control variables shown in Table 7 were sent to the EMCS of the target building, which was then offered to the building management operator. The control variables were coupled to BAS to operate the facility system, and the predicted energy consumption was compared with the measured energy consumption. 4. Results and discussion

AHU3

Doa

Dea

Dra

Doa

Dea

Dra

Doa

Dea

Dra

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ... 0.7 0.7 0 0.7 0.7 0.7 0.7 0.7 0.7 ...

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ... 0.7 0.7 0 0.7 0.7 0.7 0.7 0.7 0.7 ...

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 ...

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ... 0.7 0.7 0 0.7 0.7 0.7 0.7 0.7 0.7 ...

3.3.4.2. Predictive control. After the prediction of the building energy consumption, the MPC framework was used in the enthalpy control algorithm. Enthalpy control regulates the amount of the outdoor air supply by comparing the conditions of the return and outdoor air to provide the thermal condition of the supply air with a condition suitable for cooling. In this study, damper positions (Dra is the damper position of the return air, Doa is the damper position of outdoor air, and Dea is the damper position of exhaust air) of each AHU were calculated as control variables. Fig. 10 shows the developed framework based on BCVTB, and it consists two parts: predict and control (based on the prediction). Hence the upper part of the figure shows the predicting and the lower part the controlling. Existing enthalpy control is to control the damper position based on the current given value, whereas in the figure the enthalpy control uses the calculated control variables that are set to be used in the next timestep as the calculation of the values of the next timestep is affected by the values of the previous timestep. Since the building requires certain time to

The purpose of this study is to use a real-time BEMS data to develop a framework capable of performing the predictive control for the building in order to use energy effectively. In particular, the reliability of the prediction should be assured for the potential of energy savings; therefore, the BEMS data-based simulation was performed to improve the predictability. A co-simulation of predictive control was implemented to apply a real-time BEMS data to a simulation. This can provide operators with the integrated information (predicted energy consumption, potential energy savings, etc.) to enable them to make a decision. In addition, a well-known and normal enthalpy control was applied to the developed MPC framework. This is not that the developed framework applied a special algorithm; moreover, it is rather to apply the general algorithm. In addition, the calculated energy saving presents the potentiality, rather than the actual savings. However, the case study was carried out on a five-story building in Korea on a particular day of the year. And, the day that was being studied was of the most severe conditions, the hottest and most humid day of the year. If the potential of energy saving was drawn from another day without severe conditions, it would’ve shown savings of at least more than 9 kW h (0.5%). In other words, the reason why the study was performed on the hottest day of the year was to apply a MPC framework that would guarantee at least 9 kW h (0.5%) of potential energy that would be saved in other conditions compared to severe conditions. An Enthalpy control was applied in this study but in the future, it is expected to save much more energy by adding other control logic instead. A MPC framework will be developed based upon the results drawn from this study and will be applied to another building to thoroughly review the advantages of the framework, which is the effectiveness of predictive control in the future. In addition, the potential energy savings in the predictive control is expected to be drawn and then provided to the operator. 5. Conclusions In this study, a new method for predicting building energy consumption was designed, and an MPC framework that executes

Y. Kwak et al. / Applied Energy 155 (2015) 1–13

predictive control was developed. This method was developed via a real-time building energy simulation, where external data were input into the simulation. To complement the input of uncertain data, real-time predictive weather elements and EMCS data were used, and to complement the arbitrary events caused by the occupants, the lighting and plug energy consumption data were used. The key results of the study are as follows:

[11]

[12]

[13]

(1) An MPC framework that allows the prediction and control of energy consumption based on the real-time BEMS data was developed. (2) The predicted and measured energy consumptions were compared by statistical indices such as the hourly MBE and Cv(RMSE), showing acceptable values of 0.7% and 19.1%, respectively. (3) Through the indoor and outdoor environmental elements predicted using the real-time BEMS data, the enthalpy control algorithm was used, and the control variables were calculated, which were the damper positions of the AHUs. The simulation result after the application of the calculated control variables showed that the energy savings compared to the predicted energy consumption was about 9 kW h (0.5%).

[14] [15] [16] [17] [18]

[19] [20]

[21]

[22] [23]

This study implemented the real-time building energy simulation, which showed a high prediction rate after its application to the target building. Predictive control simulation based on the result will produce reasonable results (energy savings and control variables) as the predictive control reflecting the current condition. While such results were obtained from the simulation, the method proposed in this study was shown to be more reliable than those proposed in the past MPC studies. Moreover, although the amount of energy savings in this study was not significant, the developed method was shown to be effective in analyzing the real-time control result. Herein, the calculated control variables were offered to the building management operator for use as the decision-making data for the building operation.

[24]

[25]

[26]

[27]

[28]

[29]

Acknowledgement

[30]

This study was supported by the main project of the Korea Institute of Energy Research (Project No. B3-2431-03).

[31]

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