## 3 Simulation Results
In addition, the temperature of the water discharged into the breeding pool is another key driver; each additional degree above 12 \({}^{\circ}\)C typically increases power consumption by several kilowatts. The tight and mostly positive SHAP distribution supports the intuitive linear relationship between thermal load and compressor effort. Interestingly, when the water temperature is significantly below the threshold value (represented by Temp_in_low in Figure 11), it pulls the power prediction downward, suggesting reduced energy demand. However, temperatures close to the threshold do not significantly affect the forecast on their own. A binary indicator capturing the system's operational status is also included. When the system is active (indicator = 1, shown in red), the SHAP values are generally positive, whereas inactive states (indicator = 0, shown in blue) correspond to near-zero or slightly negative values. However, this flag is less impactful than the actual temperature deviation. Seasonal effects have a moderate influence. Higher numeric values corresponding to summer and autumn yield slightly positive SHAP values, indicating a small expected  
Figure 11: SHAP values for the ammonia cooling system. Figure 10: Comparison between the actual and predicted value across test data for the CO\({}_{2}\) cooling system. increase in power consumption during warmer months after accounting for real-time temperature. Finally, circadian patterns contribute subtle refinements to the forecast, typically in the range of \(\pm 10\) kW. Their centered distribution implies that these features help to fine-tune predictions rather than drive them. Indeed, the SHAP analysis suggests that this unit's operation is primarily reactive, focused on maintaining water temperature rather than on strategically aligned with dynamic electricity pricing. This behavior highlights the system's role in thermal regulation rather than economic optimization. A similar analysis is provided for the other main cooling system with CO\({}_{2}\) refrigerant, which can be found in Figure 12.  
Similar to the previous cooling system, the previous hour's power consumption plays a dominant role in predicting the current hour's usage. This single feature effectively summarizes various hidden factors such as heat load, set-points, defrost cycles, door openings, and brief weather anomalies that were active during the previous hour. Because thermal energy scatters slowly, a high load in the previous hour remains the strongest predictor of current consumption. As a result, the model relies on this input more heavily than any other. From an operational standpoint, this implies that smoothing spikes or pre-cooling during low-price periods not only delivers immediate savings but also sets the system into a lower operational state, potentially reducing the next hour's demand by tens of kilowatts. As expected, each additional degree above 12 \({}^{\circ}\)C contributes significantly to power consumption, increasing demand by tens of kilowatts. Unlike the earlier system, this unit exhibits higher power consumption (approximately 20 kW more) even after controlling for the actual water temperature during summer and autumn hours. This pattern is likely influenced by additional seasonal factors such as humidity, occupancy levels, or adjusted set-points. Finally, the daily rhythm contributes a moderate influence on consumption patterns, while the binary variable indicating whether the system is active has the least impact among all features. As seen, the two most influential factors in predicting the power consumption of the cooling systems are the previous hour's power consumption and the temperature of the water discharged into the breeding pools. Therefore, it is essential to examine the conditions under which the maximum prediction error occurs across different ranges of these two variables. The error is defined as the difference between the predicted and actual values (\(Error=Predicted-Actual\)).