A semi-supervised learning approach to study the energy consumption in smart buildings

IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 05/12/2021-07/12/2021.In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three submodels for merging, partition groups (classes/cluster) and migrating individuals from a group to another. For the performance evaluation, several datasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

A multi-label approach for diagnosis problems in energy systems using LAMDA algorithm

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 18-23 July 2022, Italia.In this paper, we propose a supervised multilabel algorithm called Learning Algorithm for Multivariate Data Analysis for Multilabel Classification (LAMDA-ML). This algorithm is based on the algorithms of the LAMDA family, in particular, on the LAMDA-HAD (Higher Adequacy Grade) algorithm. Unlike previous algorithms in a multi-label context, LAMDA-ML is based on the Global Adequacy Degree (GAD) of an individual in multiple classes. In our proposal, we define a membership threshold (Gt), such that for all GAD values above this threshold, it implies that an individual will be assigned to the respective classes. For the evaluation of the performance of this proposal, a solar power generation dataset is used, with very encouraging results according to several metrics in the context of multiple labels.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Analysis of customer energy consumption patterns using an online fuzzy clustering technique

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 18/07/2022-23/07/2022, Italia.Currently, there is a high rate of generation of new information about the Energy Consumption of customers. It is important the traceability of its consumption pattern evolution to determine in real-time the services of a smart energy management system. This paper analyses the evolution of the Energy Consumption Pattern of customers using the Learning Algorithm for Multivariable Data Analysis (LAMDA). LAMDA is a fuzzy approach for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class/cluster, through the contributions of all its descriptors. LAMDA can create new classes/clusters after the training stage (online learning). If an individual does not have enough similarity to the preexisting classes/clusters, it is evaluated with respect to a threshold called the Non-Informative Class (NIC) to define if it is a new class/cluster. Particularly, the algorithm of the LAMDA family used in this paper is LAMDA-RD (Robust Distance). In the paper is analyzed the patterns of the initial grouping of the data, as well as, the patterns through their evolution (traceability). For the analysis of the patterns different metrics are considers: Calinski- Harabasz Index and Silhouette Score.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Approaches based on LAMDA control applied to regulate HVAC systems for buildings

The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Autonomous cycle of data analysis tasks for scheduling the use of controllable load appliances using renewable energy

International Conference on Computational Science and Computational Intelligence, 15/12/2021-17/12/2021, Estados Unidos.With the arrival of smart edifications with renewable energy generation capacities, new possibilities for optimizing the use of the energy network appear. In particular, this work defines a system that automatically generates hours of use of the controllable load appliances (washing machine, dishwasher, etc.) within these edifications, in such a way that the use of renewable energy is maximized. To achieve this, we are based on the hypothesis that depending on the climate, a prediction can be made of how much energy will be generated and, according to the behavior of the users, the energy demand required by these appliances. Following this hypothesis, we build an autonomous cycle of data analysis tasks composed of three tasks, two tasks for estimating the required load (demand) and the renewable energy produced (supply), coupled with a scheduling task to generate the plans of use of appliances. The results indicate that it is possible to carry out optimal scheduling of the use of appliances, but that they depend on the quality of the predictions of supply and demand.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

Autonomic management of a building's multi-HVAC system start-up

Most 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 defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration 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.European CommissionJunta de Comunidades de Castilla-La ManchaMinisterio de Ciencia e Innovació

La ética en la relación atleta-club en el deporte profesional: un análisis crítico desde la filosofía de la ética de Kant, Habermas y MacIntyre

El deporte profesional agrupa individuos y organizaciones que se involucran a este desde una perspectiva ocupacional. La ética se conoce como el comportamiento de las personas, en su vida en sociedad. Kant, Habermas y MacIntyre promueven concepciones filosóficas de la ética que facilitan la comprensión de los comportamientos. La visión del deporte profesional trae consigo una serie de consideraciones éticas, que vale la pena revisar desde la óptica de estos filósofos. Si bien es cierto que en los hechos económicos y comerciales la ética debe estar presente, en la forma actual en cómo se desarrollan esos aspectos hace relevante este estudio

REPRESENTACIÓN DE LA PLANIFICACIÓN EN LOS SISTEMAS MULTI-AGENTE A TRAVÉS DE MATRICES

El proceso de planificación en sistemas multi-agentes está relacionado con la definición de secuencias de acciones que uno o más agentes deben seguir para lograr uno o más objetivos. En general, la planificación se da en tres fases: creación del plan, asignación de sub-planes o tareas a uno o más agentes, y ejecución de dicho plan. La planificación, como un esquema de coordinación en sistemas multi-agentes, podría tratarse como un problema de optimización para determinar las mejores secuencias de acción y la mejor asignación de tareas a los agentes, para lo cual se debe proponer una descripción matemática para representar dicha asignación. Este documento propone una matriz y una representación gráfica de los servicios y la asignación de recursos para la planificación en sistemas multi-agente, que permiten la fácil gestión del proceso de planificación. La descripción matemática propone varias matrices y gráficos para representar naturalmente la asignación del plan en una comunidad de agentes orientada al servicio. Esta descripción puede ser utilizada por los modelos de optimización de procesos de coordinación en sistemas multi-agentes, así como por las plataformas de implementación de comunidades de agentes, como por ejemplo, JADE (Java Agent DEvelopment Framework). La descripción matemática propuesta se ilustra en dos casos de estudio, para evaluar su capacidad para describir, en particular, su amplitud y exhaustividad