Smart Petri Nets Temperature Control Framework for Reducing Building Energy Consumption
2. Petri Nets
3. Pid Controller
- The P term () is proportional to the current value of the error e(t). For example, if the error is large and positive, the control output will be proportionately large and positive, taking into account the gain factor “”. Using proportional control alone will result in an error between r(t) signal and y(t) because it requires an error to generate the proportional response. If there is no error, there is no corrective response.
- The I term () accounts for past values of the error and integrates them over time. For example, if there is a residual error after the application of proportional control, the integral term seeks to eliminate the residual error by adding a control effect due to the cumulative historical value of the error. When the error is eliminated, the integral term will cease to grow. This will result in the proportional effect diminishing as the error decreases, but this is compensated for by the growing integral effect.
- The D term () predicts system behavior and thus improves settling time and stability of the system. It is also the best estimate of the future trend of the error, based on its current rate of change. It is sometimes called “anticipatory control”, as it is effectively seeking to reduce the effect of the error by exerting a control influence generated by the rate of error change. The more rapid the change, the greater the controlling or dampening effect.
4. User Identification
5. Smart Temperature Control Framework
- monitor the presence at the office,
- detect a new presence,
- identify the detected person,
- generate his preferred temperature,
- send this temperature as a reference signal for temperature regulation,
- guarantee that the ambient temperature in the room reaches this reference temperature,
- finally, goes into standby state or in the off state in the absence of anyone.
6. Case Study
6.1. Stage 1: User Identification
- Step 1:
- Face detection (using Viola-Jones algorithm)
- Step 2:
- Feature extraction
- Step 3:
- Face identification
6.2. Stage 2: Desired Temperature Generation Using a Supervisor PN
6.2.1. Master PN
6.2.2. Slave PN
- The first case is that the identified lecturer is , then his preferred reference temperature will be sent to the third stage (PID controller).
- The second case when is identified in the office. In this case, the preferred reference temperature sent to the third stage will be .
- In the last case the and will be present in the office simultaneously, then the reference temperature , on which and have agreed, will be sent to the third stage.
6.3. Stage 3: Temperature Regulation Based on a PID Controller
7. Simulation Results
- The user’s comfort can be observed in Figure 11 through the short response time in the temperature signal. Indeed, it can be seen that, at each change of lecturer presence in the office, the framework generates the new user’s desired temperature, and the PID controller acts on the office ambient temperature to reach the desired one quickly. Choosing a room temperature equal to 30 C in the absence of both lecturers (Stdby mode), participates in reaching the preferred user’s temperature quickly, since it is well known that the outside temperature in KSA can easily exceed 50 C during summer. This reasonable ambient temperature consumes more energy, but it is necessary to maintain an acceptable user comfort level.
- Energy saving: The comparison between the traditional system based on an On/Off signal shown in Figure 15 and the proposed smart framework illustrated in Figure 14 shows a considerable reduction in the energy consumed by the latter compared to the first one. In fact, the simulations show that using the smart framework, the reduction in energy consumption is about compared to the current On/Off controller. Indeed, using the On/Off controller, to keep the temperature around a predefined temperature of 25 C during 12 h, from 7:30 a.m. to 19:30 p.m., the total consumed energy was of kW as shown in Figure 15, while it is only of kW using the smart framework, as shown in Figure 14.
8. Framework Advantages
- Energy consumption reduction: Indeed, compared to the On/Off method used currently in the department building based on a predefined constant reference temperature throughout the day, the proposed framework allows sending each user preferred temperature. As a result, very significant energy savings are made, as shown in Figure 14. Beyond these savings, the most important is the idea that at any time of the day, the proposed smart framework consumes the exact required energy, neither more nor less.
- User’s comfort: The proposed approach responds precisely to the users’ needs. Indeed, the role of the second stage, the PN based supervisor, is to detect any presence in the office, identify the user, then send the corresponding reference temperature expressed by the identified user. This dramatically improves user comfort.
- Reactivity: One of the critical features of the proposed approach is its reactivity. Indeed, this approach has a great ability to react, very quickly, to changes in its environment. This is because through the use of a PN and a closed control loop, can simultaneously perform several functions of observation, monitoring, and correction of the system. Any changes in the presence or absence of people in the controlled space are immediately reflected in the reference temperature sent to the third stage as well as in the control signal sent to the controlled system.
- System Supervision: The fact that PN and closed-loop control are implemented in this framework gives the ability to supervise the global system in real time to ensure that the issued command is in perfect harmony with the state of the system. This avoids the propagation of component failure to other components, which provides an entirely reliable control system.
- Flexibility: The great interest of the proposed framework is its flexibility. Indeed, any necessary modification of conditions or variables used in the framework can be easily implemented in the PN and taken into account in office temperature regulation. Such modification could be for example the increasing or decreasing of the number of people using the office, or changing the preferred temperature of one or all users. In the case study discussed in this paper, if we decide to increase the number of people using the office to three, then we have to make the following changes. First, adding an additional state, with its input and output transitions, corresponding to the sending of the new user’s preferred temperature as a reference temperature (in a case he is alone in the office). Second, changing the preferred temperature on which the three users have agreed. We get, as a result, the following PN shown in Figure 17.
Conflicts of Interest
|HVAC||Heating, Ventilation and Air Conditioning|
|RNN||Random Neural Network|
|IoT||Internet of Things|
|CPN||Colored Petri Nets|
|SPN||Stochastic Petri Nets|
|TPN||Temporal Petri Nets|
|MPC||Predictive Control Model|
|HVAC||Heating, Ventilation and Air Conditioning|
|PID||Proportional, Integral, and Differential Control|
|Stdby||Stand By mode|
|P||Set of places in Petri Net|
|T (in PN)||Set of transitions in PN|
|t||is the time or instantaneous time (the present) (s)|
|is the variable of integration (takes on values from time 0 to the present t) (s)|
|System measurement variable|
|PID proportional constant|
|PID integral constant|
|PID derivative constant|
|C||Total specific heat capacity (J·kg·C)|
|Heat capacity at constant pressure J/C)|
|T (in TF)||Temperature (C)|
|Exchanging air flow (m/s)|
|A||Indoor surface area (m)|
|X||Outdoor wall thickness (m)|
|K||Thermal conductivity coefficient (W·m·C)|
|Sensor mass (kg)|
|Sensor heat capacity (J/C)|
|Convection coefficient (W/(m C))|
|Sensor area (m)|
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|An occupancy in the office||User detection|
|No occupancy in the office||No user detection|
|Stand-by mode||Working status changes from ON to Standby|
|ON mode||Working status changes from Standby to ON|
|OFF mode||Working status changes from OFF to Standby|
|User identif. process||Working status changes from Standby to OFF|
|Sending user1 desired temp. to stage3 during t = 10 min||Beginning of the identification|
|Sending user2 desired temp. to stage 3 t = 10 min||Lecturer 1 detected|
|Sending users 1 and 2 desired temp. to stage 3 t = 10 min||Lecturer 2 detected|
|Intermediary state||Lecturer 1 and 2 detected simultaneously|
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Bouazza, K.E.; Deabes, W. Smart Petri Nets Temperature Control Framework for Reducing Building Energy Consumption. Sensors 2019, 19, 2441. https://doi.org/10.3390/s19112441
Bouazza KE, Deabes W. Smart Petri Nets Temperature Control Framework for Reducing Building Energy Consumption. Sensors. 2019; 19(11):2441. https://doi.org/10.3390/s19112441Chicago/Turabian Style
Bouazza, Kheir Eddine, and Wael Deabes. 2019. "Smart Petri Nets Temperature Control Framework for Reducing Building Energy Consumption" Sensors 19, no. 11: 2441. https://doi.org/10.3390/s19112441