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Fig. 6. Energy consumption that the performance of the sensitivity based DMPC is equivalent to the CMPC performance. The decomposition and coordination strategies are demonstrated on the VAV based HVAC building.

Figure 6 Energy consumption that the performance of the sensitivity based DMPC is equivalent to the CMPC performance. The decomposition and coordination strategies are demonstrated on the VAV based HVAC building.

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In this section, we describe a benchmark school building which is used to demonstrate the proposed sensitivity based DMPC approach. The building has two floors with 8 zones having a total area 648m. The cross sectional layout for the benchmark building is shown in Figure 1. This Fig. 1. Building Distribution: two floors with 4 classsrooms each
In this section, we describe a benchmark school building which is used to demonstrate the proposed sensitivity based DMPC approach. The building has two floors with 8 zones having a total area 648m. The cross sectional layout for the benchmark building is shown in Figure 1. This Fig. 1. Building Distribution: two floors with 4 classsrooms each
Fig. 2. VAV based HVAC system air temperature. Then, the first law of thermodynamics applied to each zone is
Fig. 2. VAV based HVAC system air temperature. Then, the first law of thermodynamics applied to each zone is
where the i-th column represents h; denoting the thermal balance for i-th zone. The rows represent the variables with respect to which the sensitivity is calculated. This sensitivity matrix contains the information about the system couplings with the states and the inputs. The following section explains the methodology to exploit this information in the system decomposition. The off-diagonal coefficients in this block matrix represent the degree of the sensitivity of the state variables x (x1,...,2%,) with respect to other state variables and inputs wu (u,,...,Um)- The basic idea behind the decomposition is to partition the matrix (3) into p block diagonal form. Every block will represent the group of zones representing a subsystem. The methodology of the matrix partition ensuring the minimal loss of information is explained in detail in the next section. We write the thermal balance equations (2) for all the zones and represent them in matrix form as follows, represents the sensitivity of 7-th temperature zone with respect to the i-th input (u;). Note that inputs from the neighboring zones (u,;) will affect the the i-th zone temperature through j-th zone temperature (x;). This will be accounted in the coefficient a;;.
where the i-th column represents h; denoting the thermal balance for i-th zone. The rows represent the variables with respect to which the sensitivity is calculated. This sensitivity matrix contains the information about the system couplings with the states and the inputs. The following section explains the methodology to exploit this information in the system decomposition. The off-diagonal coefficients in this block matrix represent the degree of the sensitivity of the state variables x (x1,...,2%,) with respect to other state variables and inputs wu (u,,...,Um)- The basic idea behind the decomposition is to partition the matrix (3) into p block diagonal form. Every block will represent the group of zones representing a subsystem. The methodology of the matrix partition ensuring the minimal loss of information is explained in detail in the next section. We write the thermal balance equations (2) for all the zones and represent them in matrix form as follows, represents the sensitivity of 7-th temperature zone with respect to the i-th input (u;). Note that inputs from the neighboring zones (u,;) will affect the the i-th zone temperature through j-th zone temperature (x;). This will be accounted in the coefficient a;;.
Table 1. Numerical data values used for bench- mark building simulation
Table 1. Numerical data values used for bench- mark building simulation
Fig. 4. Temperatures response for all the zones Fig. 5. Supply airflow rates for all the zones
Fig. 4. Temperatures response for all the zones Fig. 5. Supply airflow rates for all the zones
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Computer ScienceDistributed ComputingDecision MakingEnergy OptimizationRobustness (evolution)IEEE International Conference on Computer Science and Automation EngineeringDistributed AgentsSoftware DeploymentAutomation ScienceDistributed Algorithm
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