Overview and Application of Enabling Technologies Oriented on Energy Routing Monitoring, on Network Installation and on Predictive Maintenance

Abstract

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 .

A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance

“…Concerning a unified modeling language (UML2), different design layouts were developed using the open source tool UMLet 14.2. KNIME Analytics Platform version 3.5.3 was the tool used for the implementation of the workflow of MLP-ANN [21,22,27] oriented on health status prediction. The MLP is a feed-forward artificial neural network model that maps sets of input physiological data of patients onto a set of appropriate output related to health status prediction.…”

“…The MLP approach adopts back propagation for training the network. For the training of the MLP, the neural network model (learner of the model) applied the efficient RProp algorithm [28,29] suitable for the multilayer feed-forward networks defined in [21,22]: RProp performs a local adaptation of the weight-updates according to the behavior of the error function. For plotting the analyzed MLP dataset, the "Chart Style Series" of the graphical dashboard of RapidMiner Studio Version 8.2 was used.…”

“…In this direction, ANN could predict patient physiological status [17], could be applied for predictive medicine [18], and for the prediction of heart problems [19], suggesting ANN as a good approach to predict patient health status in DSS. In particular, ANN applied in other applications exhibited high performances [20], specifically MLP-ANN provided a good match between experimental and predicted results [21], and good flexibility concerning predictive maintenance and big data analytics [22].…”

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“…The intelligent logic unit executes the algorithm which evaluates the slope of the curve at each minute by comparing it with the slope of the line representing the maximum absorption (see Fig. 4) [25]. The blue line indicates the measured electrical power of an electrical outlet connected with different loads, and the red line indicates the threshold line having a defined slope according with energy management risk.…”

“…the testing of the LSTM algorithm by considering a dataset of measured values of a network of three electrical outlets providing measured data; a complete scenario to understand prediction of global active power of the building; a correlation analysis between different attributes including active and reactive power, explaining the status of the analyzed building electrical network. The highlights of the proposed paper are the following ones: 1-application of the load prediction theory discussed in [25], together with LSTM electrical power prediction in order to increase the reliability of the prediction; 2-definition of the flowchart able to enable and disable loads characterized by two priority level (priority of the electrical outlet and load priority); 3-testing of the LSTM model showing good performance; 4-explanation of the correlation of reactive power with active one by identifying inefficient outlets. Below is summarized the features and the requirements of the whole proposed mode :…”