## 3 The Structure of the Decision Support Platform
### Forecasting Consumption Module  
At the level of industrial consumers, mainly SMEs, energy consumption forecasting provides the primary information in decision-making regarding the optimal planning of activities associated with the technological flows. Thus, energy consumption forecasting must use historical data recorded in the long periods and processed appropriately. If the number of records is small, then the estimation errors can be high and can lead to additional operating or investment costs. On the other hand, energy consumption forecasting plays an important role in the development of economic strategies for SMEs. The decision-making on the different periods (short, medium, or long), related to the planning and development of the industrial processes, requires a good knowledge of the energy consumptions from each activity and technological flow. The technical parameters of each equipment or installations should be known, and continuous monitoring represents the way to understand all energy consumption characteristics [54; 55]. The proposed method in this module uses an expert system that considers aggregation rules for the energy consumption of equipment and installations for each activity associated with the technological flow. If for the SMEs, following the detailed analysis of the technological processes, the technical characteristics, the operating periods, the number of operating cycles associated with each equipment or installation were identified, then an aggregation-based forecasting method based on an expert system (ES) can be developed to estimate the daily energy consumption. Figure 7 presents the flow chart of the proposed method. In figure, the following notations were used: the index \(e\), for equipment and installation (\(e=1,\,\ldots,\,n_{e}^{a}\)), \(n_{e}^{a}\), for the total number of equipment and installations associated with the activity \(a\), and \(n_{a}\), for the total number of activities. Figure 7: The flowchart of expert system-based aggregated method. An ES is a powerful tool used in the proposed method, having the following main components: database, knowledge base, and inference rules. It can identify the solutions to the difficult problem regarding the energy consumptions of the equipment and installations in various operating regimes that require the extensive experience of a Decision-Maker. The last two components (knowledge base and inference rules) try to model the Decision Makers' experience to establish the set of admissible solutions. The framework of an expert system is shown in Figure 8[55]. Human-machine interface (HMI) performs communication. The explanation of reasoning's module performs, as the main function, the necessity to justify and explain the stages regarding the reasoning of the expert system. The demonstration of the necessity inside the ES can be done in the initial phase (development), confirming the correctness of acquiring and using knowledge, and in the final phase, quantifying the importance and the result of the taken actions. The database is used to build inference rules. The inference engine determines which inference rules are fulfilled and taken over in the order of priorities. A code, written in the form of rules, will convert the knowledge base to solve the problem. Two matrices are the basis of the expert system. The first matrix, Equipment and Installations' Matrix (EIM), contains the equipment and installations' characteristics, initialized with the input data corresponding to the scheduled activities from the next days. The rated power, working times, according to the type of daily activity (e.g., small, medium, and high) and the absorbed power in the steady-state regime, for each equipment or installation, represent the elements in this matrix. The second matrix, Decision-Making Matrix (DMM), has a high importance in decision-making.