Early 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’s HVAC 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.

The SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) model is a mathematical model based on dynamic equations; widely used for characterization of the COVID-19 pandemic. In this paper, a different approach has been discussed, which is the development of predictive models for the SEIRD variables that have been based on the historical data collected, and the context variables to where this model has been applied to. Particularly, the context variables examined in this paper include total population, number of people over 65 years old, poverty index, morbidity rates, average age, and population density. For the construction of the SEIRD predictive models, this study encompasses a deep analysis of the dependence of these variables and also, their relationship with the context variables. Hence, before the development of predictive models using machine learning techniques, a methodology to analyze the interdependence of the SEIRD variables has been proposed. The dependence with the context variables is also discussed; to avoid the curse of dimensionality and multicollinearity problems, leading to better results and the reduction of the computational cost. Finally, several prediction models based on varied machine learning techniques and inputs are considered, these include temporal interdependence, temporal intra-dependence, and dependence with context variables. Each of the predictive models has been studied, as well as their quality of prediction. This paper focuses on the analysis of the quality of this approach, applied in Colombia, obtaining the results about the performance of the predictive models for the SEIRD variables. The results are very encouraging since the values obtained with the quality metrics are quite good for different prediction horizons.

Several works have proposed predictive models of the SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) variables to characterize the pandemic of COVID-19. One of the challenges of these models is to be able to follow the dynamics of the disease to make more precise predictions. In this paper, we propose an approach based on incremental learning to build predictive models of the SEIRD variables for the COVID-19 pandemic. Our incremental learning approach is a dynamic ensemble method based on a bagging scheme that allows the addition of new models or the updating of incremental models. The article proposes an incremental learning architecture composed of two components. The first component carries out an analysis of the interdependencies of the SEIRD variables and the second component is an incremental learning model that builds/updates the predictive models. The paper analyses the quality of the predictive models of our incremental learning approach using data of the COVID-19 from Colombia, and shows interesting results about the predictions of the SEIRD variables. These results are compared with an incremental learning approach based on random forests.

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