## 4 Discussion  
After computing the prediction, the Historian application was left in operation to store the values of all the monitored tags for a period of 7 d (the prediction is made for 7 d ahead); thus, at the end of  
Figure 9: Prediction algorithm output for test case 5.  
the 7 d for which predicted values were available, the actual values, recorded from the WWTP, were also available, facilitating an assessment of the prediction algorithm viability. For supporting an identification of conclusive aspects, in each of the seven test cases considered, the predicted values were compared to the actual values; the most significant results, considering all test cases, regarding the possibility of future optimizations and improvements in the monitored technical system, as summarized in Figure 10, are displayed in the following more detailed explanation. Figure 10 presents five OPC UA tags (CSF10_Curent, OPCTpH1 UA, TGD1_Ep, CSF7_Curent, and F2_Debit_T), whose values were identified as being related to the evolution of different weather characteristics. For each of those tags, Figure 10 illustrates the predicted value (blue lines) and the actual value (green lines) in each of the seven predicted days (the seven columns with headers containing dates). The error lines (red lines) present the prediction error for each day (computed in percent, using the following formula: E = (ABS (P - A) * 100) / A, where E = error, P = predicted value, A = actual value, and ABS = function returning the absolute value). The last column (\"Average Error\") represents the arithmetic mean of the error values computed per day of the respective tag. In the continuation of this section, the interpretation of the results presented in Figure 10 follows, with regard to the optimization possibilities of a WWTP. Tag CSF10_Curent represents the intensity (in amps) of the electric current consumed by a pump, which is used for the transfer of wastewater from mechanical treatment to biological treatment inside a WWTP. This pump intensifies in usage as more wastewater enters the system and has a similar behavior as the bypass pump. Being able to accurately predict the usage of those pumps signifies the capability to predict the usage amount of the bypass system, which also has a huge impact on both the overall power consumption of the WWTP and the overall substances consumption of the WWTP. The usage of the transfer pumps and the bypass system is dependent on rainfall for the majority of the plants, leading to the conclusion that the prediction algorithm can bring significant added value to this area. Tag OPCTpH1 UA signifies the water turbidity, measured at the exit of the WWTP, a characteristic that shows the water quality after receiving the treatment inside the WWTP. This characteristic must meet legal requirements regarding its limits, and predicting it helps in better estimates of the future consumption of substances. Tag TGD1_Ep represents the overall WWTP energy consumption, its accurate prediction being imperatively needed for any energy-related optimization. Tag CSF7_Curent represents the intensity (in amps) of the electric current consumed by an air blower that introduces oxygen into a biological basin. Estimating this value accurately opens the possibility of understanding and predicting the energy consumption of a biological basin and its weight in the overall energy consumption. Figure 10: The most significant results of the test cases. Tag F2_Debit_T signifies the water volume at the WWTP entrance, where its correct prediction allows for an accurate estimate of the overall energy consumption, the overall substances consumption, and the bypass system usage. The water volume at the WWTP entrance is directly influenced to a large extent by meteorological characteristics (especially precipitation amount).