Browsing by Author "Garcés Jiménez, Alberto"
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Item A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings.(Renewable and Sustainable Energy Reviews, 2021) Aguilar, J.; Garcés Jiménez, Alberto; Moreno, M. D.; García, RodrigoBuildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.Item Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings.(IEEE Access, 2020) Morales Escobar, L.; Aguilar, J.; Garcés Jiménez, Alberto; Gutiérrez de Mesa, José Antonio; Gómez Pulido, José ManuelControl in HVAC (heating, ventilation and air-conditioning) systems of buildings is not trivial, and its design is considered challenging due to the complexity in the analysis of the dynamics of its nonlinear characteristics for the identi cation of its mathematical model.HVAC systems are complex since they consist of several elements, such as heat pumps, chillers, valves, heating/cooling coils, boilers, air-handling units, fans, liquid/air distribution systems, and thermal storage systems. This article proposes the application of LAMDA (learning algorithm for multivariable data analysis) for advanced control in HVAC systems for buildings. LAMDA addresses the control problem using a fuzzy classi cation approach without requiring a mathematical model of the plant/system. The method determines the degree of adequacy of a system for every class and subsequently determines its similarity degree, and it is used to identify the functional state or class of the system. Then, based on a novel inference method that has been added toLAMDA, a control action is computed that brings the system to a zero-error state. The LAMDA controller performance is analyzed via evaluation on a regulation problem of an HVAC system of a building, and it is compared with other similar approaches. According to the results, our method performs impressively in these systems, thereby leading to a trustable model for the implementation of improved building management systems. The LAMDA control performs very well for disturbances by proposing control actions that are not abrupt, and it outperforms the compared approaches.Item An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building.(Energies, 2020) Aguilar, Jose; Ardila, Douglas; Avendaño, Andrés; Macías, Felipe; White, Camila; Gómez Pulido, José; Gutiérrez de Mesa, José; Garcés Jiménez, AlbertoEarly fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’sHVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.Item Analysis of Artificial Neural Network Architectures for Modeling Smart Lighting Systems for Energy Savings.(IEEE Access, 2019) Garcés Jiménez, Alberto; Castillo Sequera, José Luis; Corte Valiente, Antonio del; Gómez Pulido, José Manuel; Domínguez González-Seco, Esteban PatricioCurrently, population growth is global and tends to concentrate in large cities, which increases the demand for illuminating public spaces for safety, visual orientation, aesthetic considerations, and quality of life. The undesirable side effects are increase in energy consumption and light pollution. The current tools used for designing public lighting systems are not suitable for optimizing multiple objectives in addition to energy savings, and these solutions could provide for a more sustainable environment. The application of evolutionary optimization techniques seems to be growing rapidly because of the nonlinearity of the model behavior and the nonproprietary nature of the algorithms, which are considered as black box systems. This paper develops a data model for these types of optimizers, analyzing the ability of different arti cial neural network (ANN) architectures to simulate a simple public lighting design by measuring the performance with respect to the tness function, training speed, and goodness of t with a dataset generated with different conditions. The architectures selected in this paper are those with multilayer perceptrons (MLPs) with different hidden layer con gurations using different numbers of neurons in each layer, which have been analyzed to determine the con guration that best ts the purpose of this work. The data for training the ANNs were generated with a recognized open-software platform, DIALux. The experiments were repeated and analyzed to determine the variance of the results obtained. In this way, it was possible to identify the most appropriate number of iterations required. The results show that better precision is obtained when using the Levenberg Marquardt training algorithm, especially when the ANN architecture has fewer neurons in the hidden layer.Item Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings.(IEEE Access, 2019) Aguilar, J.; Garcés Jiménez, Alberto; Gallego Salvador, Nuria; Gutiérrez de Mesa, José Antonio; Gómez Pulido, José Manuel; García Tejedor, Álvaro JoséThis article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.Item Autonomic Management of a Building's Multi-HVAC System Start-Up.(IEEE Access, 2021) Aguilar, Jose; Garcés Jiménez, Alberto; Gómez Pulido, José Manuel; Rodríguez Moreno, María Dolores; Gutiérrez de Mesa, José Antonio; Gallego Salvador, NuriaMost studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the ``Autonomic Cycle of Data Analysis Tasks'' concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the de ned operational setpoint, and if that is not the case, another task for recon guring the operational mode of the multi-HVAC system to redirect it. The rst task uses machine learning techniques to build detection and prediction models, and the second task de nes a recon guration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.