## 3 Results  
With the purpose of testing and validating the newly implemented features described in the previous section of this paper, multiple test cases were considered in the water industry, regarding a real WWTP, owned and operated by the local water company.  
The tests were conducted by making use of the most recent Historian application version available, which included the implementation of both level 1 and 2 of the reference architecture, while being installed on the Raspberry Pi platform.  
Figure 7 details the seven test cases that were considered for validating the second level of the reference architecture implementation. Each test case is covered over a period of time where data are collected (a set of relevant process data from a WWTP can contain 200-2000 tags, although the total number of tags from a WWTP can exceed 6000). From the entire set of tags, the monitored tags are defined as relevant to predict regarding the weather evolution. The number of analyzed tags for value prediction was 26 in all seven test cases. The 26 tags were chosen by the authors as being the most relevant and having the most potential of being used in any future optimization, representing mostly electric current consumptions for different pumps and air blowers and different water volumes inside the treatment plant and water quality indicators, such as turbidity.  
Initially, the Historian application was used to store the measured values of the chosen tags for the time period presented in Figure 7, a task that did not involve any proactive features and could have been carried out by any classic Historian application. Afterward, the proactive features were assessed by executing the predictive algorithm for each test case, using the GUI controls illustrated in Figure 2 above. The inputs for the test execution included historical weather data alongside the stored values of the tags from the monitored system and started with the execution of the first level algorithm (the improved version described in the previous section, deriving from that implemented in [26]), which generated as output the dependencies graphs (a different one for each test case), such as the example depicted in Figure 8 (corresponding to test case 7). In building the representation from Figure 8, only the tags (and their corresponding relations) that were found dependent on at least one weather characteristic were considered, where a drawing approach containing all the monitored system tags (and their corresponding relations) made it difficult to follow.  
Figure 6: WWTP process constraints in the Historian Process Editor.  
Figure 8: The dependencies graph for test case 7.  
Figure 7: The 7 test cases considered for validation of the implementation.  
The dependencies graphs (such as that shown in Figure 8) were used as input, in addition to the weather forecast (obtained from [28]) and the latest tags values at the moment of analysis, for the predictive algorithm described in the previous section. The outcome consisted of the algorithm successfully computing the predicted values of each of the 26 tags from the monitored system for the following 7 d starting from the execution date, in each of the seven test cases. Basically, the developed prediction algorithm accurately estimated the impact the forecasted weather will have on the technical system, by identifying and understanding (from the historical stored data) the relations that exist between the weather and the technical system tags. An example of the numerical results of the algorithm execution (for test case 5) is presented in Figure 9, where the illustrated pdf document has been exported directly from the Historian application.