## 3 System Architecture
Moreover, this study managed three types of adaptation: adaptation type 1, adaptation type 2, and adaptation type 3. The purpose of adaptation of this study was to improve the similarity between the new problems and the cases so that the new problems were used as the basis of the slight-adapting method. Figure 10 is a demonstration of slight adaptation in which a certain type of the value of cases (\(V_{C}\)) was larger than the value of problems (\(V_{P}\)). Its challenge was how to shorten the distance between these two values. In this study, the average value was used to adapt \(V_{C}\). The new value generated after adaptation was named \(V_{AV}\), as shown in Equation (5). Equation (6) is the average of characteristic attributes (\(i\)) of the problems and the cases, which also represent the solution of the new adapted cases. In this equation, \(P\) represents the problems; \(C\) means a specific case in the recommended solution; \(V(P_{i})\) represents the value of characteristic attributes of the problems; and \(V(C_{i})\) means the value of characteristic attributes of the cases. Equation (6) is the average conversion formula that could get the solution content of new case \(C_{Ad}\) after adaption. \[V_{AV}=(V_{P}+V_{C})/2,\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adaptation Condition** & **Adaptation Type 1** & **Adaptation Type 2** & **Adaptation Type 3** \\ \hline single (multiple)-like & & & \\ (0.75 \(\geq\) SR(V) \(>\) 0.85) & V\({}_{C}\) \(>\) V\({}_{P}\) & V\({}_{C}\) \(=\) V\({}_{P}\) & V\({}_{C}\)\(<\) V\({}_{P}\) \\ \hline \hline \end{tabular} Note: SR(V) is the similarity value of new problems (P) and cases (C); V\({}_{P}\) is the value of new problems; V\({}_{C}\) is the value of cases.  
\end{table} 
Table 5: Case adaptation-analysis of a single type of sensor. \[C_{Ad}=AV_{i}(P,C)=\sum_{i=1}^{n}\left(\frac{V(P_{i})+V(C_{i})}{2}\right), \tag{6}\]  
where \(C_{Ad}\) is the new case that has been adapted; and \(V_{i}\)(\(P\),\(C\)) is the problems and cases of the \(i\)th attribute. By substituting \(C_{Ad}\) into Equations (2)-(4), the new similarity values after adaptation can be calculated, and the one with the highest new similarity value was the recommended solution for energy-saving operation. The case adaptation of this study (substitutional adaptation) was a slight adaptation of values based on not changing the case structure. In this adaptation, the new problems (each type of sensors and their corresponding values) and the candidate cases (the corresponding type of sensors and values) were adapted slightly, so as to improve the similarity between cases. This study defined the application \"case retainer\" as the way to generate independent-periodical cases to explore a distinctive method of retaining cases, i.e., this is one of the important steps in sustainable processing. The huge historical data in the raw database were fully used, and so this study transformed the case retainer from a real-time application into a periodical independent application, which was different from the characteristic of cycling of the traditional case-based reasoning method. In short, to realize the difference between this case-based reasoning system and traditional case-based reasoning systems, we create an application that is particularly suitable for the energy-saving information system of this study. Figure 11 shows the operational flow of the case retainer. In this flow, the case monitor periodically retrieves the \"unconverted\" historical information (raw data) to WIAS with a system-defined cycle time (like two hours). The time was then converted into semantic words based on the Chinese sexagenary cycle, and via sorting, the common information was taken out as useful information. Finally, the relevant information was converted into semantic words, denoting the action in which the semantic case was retained into the case base has been completed. Figure 10: Case adaptation-analysis of a single type of sensor.