## 5 Conclusions and Future Work  
Supporting SMEs and the promotion of energy efficiency measures are essential for economic progress because, at the level of the European Union, the SMEs represent 98% of the total number of businesses. Many SMEs have expressed in the last period their interest in the implementation of measures to reduce energy consumption because the energy efficiency is not only a tool to save money and resources but also a need for flexible adaptation to the own needs. The SMEs have needed to implement this plan by software tools to monitor and control the consumption and to identify solutions that allow the adoption of measures to increase energy efficiency. This paper presents a new vision on the energy consumption management at the SMEs, integrated into a decision support platform, with technical and economic benefits on increasing energy efficiency. The proposed platform has four modules which include Artificial Intelligence techniques (clustering-based unsupervised learning and expert systems) with characteristics that differentiate it and offer more advantages compared to the other similar products or approaches which treated the energy management at the SMEs: (i) _The data management module_ is based on the data mining techniques that extract the technical details on the energy consumptions and cost used to develop production scheduling strategies. (ii) _The profiling module_ uses an efficient profiling-based method based on clustering techniques to determine the typical energy consumption profiles (TECPs) assigned to different activity types necessary for establishing the type of tariffs that will lead to the reduction of energy bills and optimally planning of the activities. (iii) _The forecasting module_ has implemented an original approach that uses an expert system based on the rules If-Then to forecast the energy consumption of each installation and equipment. The total energy consumption at the SME level is forecasted by aggregating all individual energy consumptions of the equipment and installations. (iv) The _production scheduling module_ allows the flattening of the energy consumption profiles, using a fast searching algorithm to find the optimal solutions. The platform can be successfully implemented, but with possible disadvantages (limitations) in the use of the platform at the maximum potential by some SMEs. The first refers to the profiling module, which can be used only if the SME has a smart meter with remote communication. The second is associated with the expert system, where the rules must be developed by qualified staff in energy management. The staff must know the technological processes, the operating regimes, and the characteristics of each equipment and installation very well. The third is linked by the forecasting module, where the same qualified staff must analyse the data from the current and measurements to determine the working times and associated energy consumptions. However, as stated, these three disadvantages are minimized by a qualified human resource, which can ensure efficient energy management to the SMEs using all functions of the platform. The proposed decision support platform was tested at a small enterprise from Romania with the activity field of the repair of the vehicles. The obtained results demonstrate the ability of the platform to offer the support to the Decision-Maker in solving or understanding the following issues: the planning strategies of the SME based on the evolution of the energy consumption, the intensity of activities through the TECPs, the energy consumption forecasting for different periods (day, week, or month) depending on the scheduled activities, and the production scheduling such that the energy consumption profile to be as flat as possible.