Energy routers are recent topics of interest for scientific community working on alternative energy. Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are indicated in this work as potential technologies for the installation testing and tools for predictive maintenance of energy networks, while thermal simulation, image post-processing and data mining improve the analysis of the prediction process. Image post-processing has been applied on thermal images and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network-ANNobtaining outputs based on measured data. The paper proposes some tools procedure and methods supporting the Building Information Modeling-BIM-in smart grid applications. Finally we provide some ISO standards matching with the enabling technologies by completing the overview of scenario .

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.

In this paper is analyzed a case study of an industry project concerning irrigation decision support system (DSS) suitable for precision agriculture applications. In particular, a first prototype irrigation module has been developed by testing different components. The prototypal system concerns the irrigation management by reading field and weather values and, by enabling electrovalves through cloud control. A web panel will monitor in real time all sensors data, besides the DSS will activate or disactivate the irrigation pipelines. The irrigation decision is performed by comparing the measurements with pre-set threshold limits of sensor values and by analyzing predicted weather data. The paper describes in details the network design and implementation by discussing the sequence diagram describing the DSS data flow. Finally is proposed the DSS algorithm by discussing the DSS logic and its first implementation. The proposed DSS behaves as an engine processing simultaneously multiple parameters. The goal of the paper is to prove how potentially a microcontroller can perform a DSS which can be customized for different cultivations.

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