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Ramallo González, Alfonso Pablo

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Ramallo González, Alfonso Pablo
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Universidad de Murcia. Departamento de Ingeniería de la Informacióny las Comunicaciones
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    Commissioning of the Controlled and Automatized Testing Facility for Human Behavior and Control (CASITA)
    (MDPI, 2018-08-27) Rodríguez Rodríguez, Ignacio; González Vidal, Aurora; Ramallo González, Alfonso Pablo; Zamora Izquierdo, Miguel Ángel; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Human behavior is one of the most challenging aspects in the understanding of building physics. The need to evaluate it requires controlled environments and facilities in which researchers can test their methods. In this paper, we present the commissioning of the Controlled and Automatized Testing Facility for Human Behavior (CASITA). This is a controlled space emulation of an office or flat, with more than 20 environmental sensors, 5 electrical meters, and 10 actuators. Our contribution shown in this paper is the development of an infrastructure-Artificial Intelligence (AI) model pair that is perfectly integrated for the study of a variety of human energy use aspects. This facility will help to perform studies about human behavior in a controlled space. To verify this, we have tested this emulation for 60 days, in which equipment was turned on and off, the settings of the conditioning system were modified remotely, and lighting operation was similar to that in real behaviors. This period of commissioning generated 74.4 GB of raw data including high-frequency measurements. This work has shown that CASITA performs beyond expectations and that sensors and actuators could enable research on a variety of disciplines related to building physics and human behavior. Also, we have tested the PROPHET software, which was previously used in other disciplines and found that it could be an excellent complement to CASITA for experiments that require the prediction of several pertinent variables in a given study. Our contribution has also been to proof that this package is an ideal “soft” addition to the infrastructure. A case study forecasting energy consumption has been performed, concluding that the facility and the software PROPHET have a great potential for research and an outstanding accuracy.
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    Empirical study of massive set-point behavioral data: towards a cloud-based artificial intelligence that democratizes thermostats
    (IEEE Computer Society, 2018-07-30) Ramallo González, Alfonso Pablo; González Vidal, Aurora; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
    The research showed in this document consisted on monitoring 546 air conditioners of individual offices located in two large buildings for the later evaluation of the behaviors of users with respect to their controllers. Data was collected over 14 months and provided important insights about the phenomenon. It was seen that users can be separated in two groups, one that likes to interact with the controllers often and change the temperature at least once a week and another that interact less. It was seen that the variability of users with respect to the thermostat values they prefer is high, and that this should be taken into account when creating a “one fits all” solution. Also, it appears that adaptive thermal comfort theories that suggest users want lower temperatures in cold months are not reflected on the set-points chosen. In addition, we have seen that people interacting more with the controllers tend to waste less energy, this would be interesting if an app to interact with the user for this purpose is design. More communication with the user may imply less energy wasted.
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    Publication
    Open Access
    Nomograms for de-complexing the dimensioning of off-grid PV systems
    (Elsevier Ltd., 2020-07-28) Ramallo González, Alfonso Pablo; Loonen, Roel; Tomat, Valentina; Zamora Izquierdo, Miguel Ángel; Surugin, Dmitry; Hensen, Jan; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
    There is a need to move the building stock towards a more energy self-sustained and self-reliant one. In most countries, photovoltaic installations have great potential to make dwellings energy-autonomous. To do so, the PV installation has to be coupled with a battery system capable of providing energy for periods no solar resource. Considering how many systems will need to be dimensioned and installed to achieve the objectives of reducing the carbon footprint of the built environment, sizing tools will have to be used by installers and other blue-collar specialists. This paper shows a methodology that uses modelling and simulation for the creation of dimensioning charts (nomograms), that could be used for sizing PVsystems. The method has been tested in two locations and it seems to be accurate and robust, despite uncertainty in demand profiles. Furthermore, a survey was conducted to evaluate users’ response to the method. The results show that, although the tool may not be appealing to certain users, it achieves its goal fully: all participants managed to size the installation correctly in all cases, regardless of their level of training or expertise.
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    Publication
    Open Access
    An open IoT platform for the management and analysis of energy data
    (Elsevier, 2019-03-01) Fernando Terroso-Sáenz; González Vidal, Aurora; Ramallo González, Alfonso Pablo; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Buildings are key players when looking at end-use energy demand. It is for this reason that during the last few years, the Internet of Things (IoT) has been considered as a tool that could bring great opportunities for energy reduction via the accurate monitoring and control of a large variety of energy-related agents in buildings. However, there is a lack of IoT platforms specifically oriented towards the proper processing, management and analysis of such large and diverse data. In this context, we put forward in this paper the IoT Energy Platform (IoTEP) which attempts to provide the first holistic solution for the management of IoT energy data. The platform we show here (that has been based on FIWARE) is suitable to include several functionalities and features that are key when dealing with energy quality insurance and support for data analytics. As part of this work, we have tested the platform IoTEP with a real use case that includes data and information from three buildings totalizing hundreds of sensors. The platform has exceed expectations proving robust, plastic and versatile for the application at hand.
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