Research Outputs
Development of an IoT-Enabled Smart Electricity Meter for Real-Time Energy Monitoring and Efficiency
Smart meters play an important role in energy management systems as they provide essential parameters for real-time monitoring, protection, and control that enable informed decisions for the end-users and the utility grid. However, available systems are high-cost solutions with different hardware and software, which provide limited measuring parameters with certain accuracy. This work aims to develop and implement an innovative smart electricity meter (SEM) system that surpasses conventional designs by incorporating advanced features like noninvasive sensors, manual signal calibration, and flexible communication modes. The developed SEM supports real-time data transmission via IoT and provides superior accuracy in measuring harmonics and frequency, addressing key challenges in energy monitoring. This work contributes to real-time energy monitoring and energy management systems in residential and industrial applications.
Monitoring of thermal comfort and air quality for sustainable energy management inside hospitals based on online analytical processing and the internet of things
There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform’s data model, which facilitates operational and strategic decision making in complex hospitals.