## 3 Energy-Saving Optimal Control Strategy of Refrigeration Station
### Edge Node Fuzzy Neural Network Control Algorithm
#### 3.6.4 Fuzzification of Fuzzy Neural Networks and Their Fuzzy Rules  
The first layer simply propagates the input variables towards the next layers. In the first layer, the main parameters of the input layer include the following: chiller parameters and their rate of change (operating power, chilled water flow, chilled water inlet temperature); pump parameters and their change rate (chilled pump operating frequency, cooling pump operating frequency, chilled pump power, cooling pump power); the parameters of cooling towers and their change rate (operating frequency and power); indoor environmental parameters and their change rate (terminal load and inlet water temperature). Corresponding to the pumps, chillers, and cooling towers of the specific controller, the main parameters of the input layer in their respective controllers, the control weight is different. Fuzzification  
Gaussian membership functions with uncertain standard deviation are implemented into the second layer (fuzzification layer) in order to perform the fuzzification of the inputs. According to the operating conditions of the air-conditioning refrigeration unit, the exact universe of discourse for all fuzzy variables can be set. The exact discourse for fuzzy variable \(e(k)\) is \([-5,5]\), and for variable \(ec(k)\), it is \([-3,3]\). There are three output variables, \(\Delta K_{P}(k)\), \(\Delta K_{I}(k)\) and \(\Delta K_{D}(k)\), and their exact universes of discourse are all \([-1,1]\). After calculation, the fuzzy universes of discourse for fuzzy variables \(e(k)\) and \(ec(k)\) are \([-2,2]\), and the universe of discourse is discontinuous, forming five levels, with specific values of \([-2,-1,0,1,2]\). The fuzzy universe of discourse of the fuzzy output variable is \([-3,3]\), with specific values of \([-3,-1.5,0,1.5,3]\). Fuzzy subset T = [NB (Negative Big), NS (Negative Small), ZO (Zero), PS (Positive Small), PB (Positive Big)]. The nodes in the third layer (reasoning layer) are used to perform precondition matching of the fuzzy logic rules. The fourth layer implements the fuzzy operation and integrates the fuzzy rules which have the same consequences. The main target control parameters of the output layer are the three tuning parameters of the PID controller. The structures of the training and prediction neural networks are identical: the input layer consists of 2 neural nodes, the hidden layer consists of two layers with 10 and 25 neural nodes, respectively, and the output layer consists of 3 neural nodes representing the three regulatory parameters of the PID controller. As shown in Figure 11, the fuzzy neural network structure, determined by the training neural network and its prediction model, predicts the real-time dynamic cooling capacity demand based on the operating conditions of the refrigeration system and the terminal. After analyzing the prediction model provided by the training neural network, the required PID controller adjustment parameters are output, ensuring that the control system operates in optimal conditions and achieves the best energy-saving performance. The fuzzy rule settings are based on the following: (1) when \(\Delta EE\) (\(EER_{set}[k]-EER[k]\)) is large and \(\Delta Q\) (\(Q_{pred}[k]-Q_{ch}[k]\)) is small, the main task of the control system is to eliminate the efficiency ratio deviation, so \(\alpha\) should be small; (2) when \(\Delta EE\) is small and \(\Delta Q\) is large, the main task of the control system is to eliminate the cooling capacity deviation, so \(\alpha\) should be large. Referring to the operation rules of the refrigeration station and the actual control operation experience, the precision of the controlled variables is continuously adjusted during the simulation process, and the fuzzy rules for the three output parameters (\(\Delta K_{P}(k)\), \(\Delta K_{I}(k)\) and \(\Delta K_{D}(k)\)) of the PID controller, as shown in Table 1, are obtained.