Abstract
Energy consumption is one factor of risk in the medium term around of the world, that can be minimized by efficient use of electrical devices include its disconnection after use. This article presents a system focused on smart homes, where the concepts of Internet of Things and Artificial Intelligence are applied in the designing of a system that allows a user from a web application to disconnect and connect an electrical network in a node. From the power of the device, time of use and consumption of this, an artificial neural network was designed and trained with the backpropagation algorithm, to discriminate between seven categories (fridge, TV, iron, dryer, lamp, computer and washing machine). A percentage of accuracy of 98.914% was obtained in the training of the network and, thanks to the feedback of the user in the web application, 99.369 and 99.174% were obtained in two cases of retraining of the network.
Highlights
With the demographic increase around the world, electrical energy requirements will imply an exponential demand, many studies are abording this subject, like (Beretta et al, 2020) where that explore pattern recognition techniques to predict electrical consumption
Firebase Presentation and Data Tier The last stage of the system focuses on designing an application that serves as the basis for the electrical disconnection, and in addition to this in case the artificial neural networks (ANN) does not identify the appliance that was connected, it may request information from the user that allows to improve the learning of the network
The implementation of a central system, in this case, raspberry pi and a node, opens the doors to the design of centralized modular systems focused on smart homes
Summary
With the demographic increase around the world, electrical energy requirements will imply an exponential demand, many studies are abording this subject, like (Beretta et al, 2020) where that explore pattern recognition techniques to predict electrical consumption. Based on the training of a hidden layer by increasing the database, it is possible to show that the greater the number of data, the better the behavior of the network In this case, with 50 new data per category, 97.879% accuracy was obtained in the classification of the training data. 3. Firebase Presentation and Data Tier The last stage of the system focuses on designing an application that serves as the basis for the electrical disconnection, and in addition to this in case the ANN does not identify the appliance that was connected, it may request information from the user that allows to improve the learning of the network. The home appliance panel is only displayed when the ANN classifies an element, so the user selects the item that was connected, for the ANN to confirm if it is classified in the correct category or it is necessary to update its database and retrain the network so that if the device is reconnected the ANN will classify it in the indicated category
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