Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.

BACKGROUND Quite often, patients arrive to consultation when the symptoms of an infectious disease are already serious, forcing doctors to divert them to the emergency services. Particularly, the possible anticipation of the diagnosis -prognostic- for institutionalized people would lead to soften the treatment, increasing resident’s wellness and alleviating the degradation of the emergency services. Big data, mobile communications, cloud services or machine learning technologies applied in medicine -e-Health- assist practitioners with efficient tools. OBJECTIVE This article describes a new data collection system for predicting infectious diseases in elderly people, supporting future telecare and medical recommender applications. METHODS The system provides a medical database updated with vital signs that nurses take with medical sensors from residents. The Cloud database is accessible with a flexible microservices software architecture. RESULTS The e-Health system components are cost-effective, leading to massive implementations for servicing disadvantaged areas. The scalable architecture is prepared for big data applications that may extract valuable knowledge patterns for medical research. CONCLUSIONS The innovation relies in the combination of advanced e-Health technologies and procedures that delivers ubiquitously available quality data to provide multifaceted scalable low-cost applications to improve resident’s wealth and release public health care services.