## 4 Application Verification and Analysis  
1. In order to verify whether the proposed energy-saving measures and the algorithm of the control system can meet the actual needs of the project, according to the schematic diagram of the control system of the refrigeration station, as shown in Figure 1, the main power-consuming equipment of refrigeration station before the energy-saving transformation is shown in Table 2.  
(2) Experimental running conditions and the expected energy-saving effect. The original refrigeration station did not use bidirectional variable flow technology, and its control used simple PID control. As shown in Figure 3, this paper adopted bidirectional variable flow technology and used a fuzzy neural network to optimize the three parameters of the PID controller. The verification experiment collected data from sensors installed at the public building site, with a 5-min sampling period. A database of 4000 samples was selected from August to September 2023 (historical data before the retrofit) and 2024 (after the energy-saving retrofit) to build the load forecasting model and energy efficiency ratio forecasting model. The building cooling load was calculated from the supply and return water temperatures measured by the sensors, and the system energy efficiency ratio was calculated from the measured system power, chiller pump frequency, cooling pump frequency, and cooling tower fan frequency. The EER distribution curves of the refrigeration station before and after the retrofit are shown in Figure 12 as EER1 (traditional PID) and EER2 (fuzzy neural network), respectively. From the analysis of Figure 12, it can be seen that before the refrigeration station was retrofitted, traditional PID control technology was used, and no chilled water bidirectional variable flow technology was introduced, so the EER was mostly around 3.6, only a few reaching above 4.1, indicating a low overall system EER. After the energy-saving retrofit, the chilled water system adopted bidirectional variable technology, and a self-learning fuzzy neural network algorithm was introduced to optimize the output parameters of the PID controller. Compared to the original PID control system, the EER was increased from 3.6 to 4.1, and the improvement ratio of EER was about 13.9%. This indicates that the combination of bidirectional variable flow technology and the fuzzy neural network algorithm can be used for energy-saving optimization control in actual refrigeration stations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equipment** & \begin{tabular}{c} **Energy Consumption** \\ **(KW/h)** \\ \end{tabular} & \begin{tabular}{c} **Water Flow (m\({}^{3}\)/h)** \\ **Chilled Water Cooling Water** \\ \end{tabular} & \
\begin{tabular}{c} **Refrigeration** \\ **Capacity (KW)** \\ \end{tabular} &
\begin{tabular}{c} **Quantity** \\ \end{tabular} \\ \hline Centrifugal chiller 1 & 600 & 620 & 790 & 3700 & 1 \\ Centrifugal chiller 2 & 168 & 160 & 200 & 940 & 1 \\  & 122 & & 1100 & & 2 \\ Cooling water pump & 22 & & 155 & & 1 \\  & 110 & & 1000 & & 2 \\ Cooling tower & 8 & & 180 & & 3 \\  & 11 & & 230 & & 8 \\  & 66 & 800 & & & 2 \\ Primary chilled pump & 27 & 620 & & & 2 \\  & 10 & 124 & & & 1 \\  & 42 & 420 & & & 3 \\ Secondary chilled pump & 26 & 300 & & & 1 \\  & 31 & 245 & & & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main power consumption equipment of the refrigeration station before retrofit. Based on the actual operation status and the expected energy-saving target, the retrofit investment for the project is approximately 0.67 million yuan. The annual power consumption and energy-saving effects before and after the retrofit, under the same load conditions of the refrigeration station equipment (as shown in Table 3), are presented in Table 4.  
It can be seen from Table 4 that the energy-saving rate is 11.67%. If the electricity price is set at 0.8 yuan per kilowatt-hour, the annual saving cost is about 0.3382 million yuan.