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In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.

international journal on soft computing, artificial intelligence and applications (online)/international journal on soft computing, artificial intelligence and applications

INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MINING ENGINE ORIENTED TO BUILDING ELECTRICAL MANAGEMENT