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Espinosa Loera, Ricardo Abel
Intelligent Management System for Micro-Grids using Internet-of-Things
An Intelligent Water Consumption Prediction System based on Internet of Things
2021
,
Gutiérrez, Sebastián
,
Medina, Guillermo
,
Ponce, Hiram
,
Espinosa Loera, Ricardo Abel
The current research work proposes the design of an intelligent platform for managing the generation, distribution, transmission, commercialization and consumption of electricity, allowing the decision-making process aimed at reducing costs and maximizing the use of energy resources. The technological proposal is a system of information made of integrated sensors. This creates an electrical real-time network that share energy in the cloud through LPWAN networks. Later, the data from the sensors are received in an intelligent platform (Max4 IoT) that employs super-computing systems and artificial intelligence for the analysis of individual and aggregated data. The system is able to learn about the electrical network, knowing the consumers’ behavior and energy trends, allowing to generate responses based on specific situations by sending SMS alerts and emails about abnormal situations or programmed tasks. In addition, the system allows the generation of reports on the network status in real-time and commands control signals on the switching on-and-off of equipment in response to consumption peaks and/or power supply outages. The platform allows interaction with distributed generation systems and micro-grids, through its mobile or web interface, increasing user interaction with the electrical system and the inclusion of renewable energy sources, thus influencing a better quality of service provided to the end user. © 2021 IEEE.
2020
,
Gutiérrez, Sebastián
,
Ponce, Hiram
,
Espinosa Loera, Ricardo Abel
This work presents the development of a measurement system for water consumption based on the Internet of Things concept. In this paper, we propose a supervised learning method namely artificial hydrocarbon networks (AHN) to predict water consumption one hour ahead. A Hall effect sensor was used to obtain the water flow value through an embedded system and to show it in an interface developed in Visual Studio. For that, the embedded system sent the data in real time to a database in Firebase using the JSON communication protocol. There, the consumed water flow is stored periodically. Experimental results of the supervised learning model conclude that AHN model predicts the conditions for efficient consumption with an average root-mean squared error of 2.4924 liters per hour. © 2020 IEEE.