## 3 The Structure of the Decision Support Platform  
Four interdependent modules associated with the steps used in an efficient management process are integrated into the platform, see Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **Database** & **Consumption** & **Consumption** & **Schedule and** & **Cost** & **Decision** \\
**Reference** & **Management** & **Profiling** & **Forecasting** & **Optimization** & **Analysis** & **-Making** \\ \hline
[24; 42; 47] & Yes & No & No & No & No & Yes \\ \hline
[25; 27; 30] & Yes & No & No & Yes & No & Yes \\ \hline
[26] & Yes & Yes & Yes & No & No & Yes \\ \hline
[28; 29] & Yes & No & No & Yes & Yes & Yes \\ \hline
[31; 32] & No & No & No & Yes & Yes & Yes \\ \hline
[33; 34; 35] & No & No & No & No & Yes & Yes \\ \hline
[36; 41] & Yes & No & No & No & No & No \\ \hline
[37; 38] & Yes & No & No & Yes & Yes & Yes \\ \hline
[39; 40] & Yes & No & Yes & No & Yes & Yes \\ \hline
[43; 47] & Yes & No & Yes & Yes & No & Yes \\ \hline
[44; 46] & Yes & Yes & No & Yes & No & No \\ \hline
[45] & Yes & No & No & No & Yes & No \\ \hline Proposed approach & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between proposed approach and the literature.  
Figure 4: The structure of decision support platform.  
The platform has applicability in the process of monitoring, control, and optimization of energy consumption, respectively cost reduction; it has the following features associated with efficient energy management: description and characterization of the SME through the characteristics of energy consumption by categories of industrial consumers, regardless of the field of activity of SMEs; the establishment of the correlations between energy consumptions, respectively consumption patterns; profiling and forecasting of energy consumption; and optimal production scheduling for flattening the energy consumption profile.  
The platform allows the SME to manage the development of technological processes. Thus, the platform ensures the continuity of the technological processes, helping the SMEs to save money for useless updates. On the other hand, because the industrial processes are never static, the platform was built also to be adaptable. If the SMEs will adopt modern technologies and develop new processes to maintain their activity, the integration will be done easily in the database of the platform for new equipment and installations. Not least, the platform offers exclusive solutions, namely, what works for one SME may not work for the other.  
Artificial intelligence-based techniques were proposed inside each module of the platform to understand the evolution and trend of energy consumption and identify solutions which minimize the impact of the energy bills: _The data management module_ is based on the data mining technique that extracts the technical details on energy consumptions used to develop production scheduling strategies; _The profiling module_ integrates an original approach based on clustering techniques to determine the typical energy consumption profiles assigned to different activities; _The forecasting module_ contains a new approach that uses an expert system to forecast the total energy consumption at the SME level; and _The production scheduling module_ includes a novel approach based on the combinatorial optimization for daily consumption programming and determining the optimal solutions regarding the flattening of daily energy consumption profile with the benefits on the cost of energy bills.  
All modules use procedures that allow the interaction between them, ensuring the communication of information and the synchronization of executions, to assist the Decision-Maker in the identification of the optimal solutions associated with an efficient management process. Details on each module are presented in the following subsections.