In the current work, the artificial intelligence techniques multilayer perceptron neural network (MLP), radial basis function neural network (RBF), and group method of data handling (GMDH) were used to estimate the indoor temperature ($T_{\mathrm{int}}$ ) in buildings under tropical climate conditions. The data used for modeling correspond to experimental samples measured during one year, considering as a case study the installation of a university laboratory. The models were developed utilizing as independent variables the climatological measurements of solar radiation, wind speed, outdoor relative humidity, and environmental temperature, and the working hours and occupancy of the building. The training, statistical comparison, and modeling performance were conducted through a computational methodology to obtain the best estimation architectures for each technique. The statistical parameter applied in the study were the root mean square error (RMSE) and the coefficient of correlation (R). Results reported the MLP as the technique with the best estimation accuracy ($\mathrm{R}=93.08\%$ and $\mathrm{RMSE}=1.0166$ for training, and $\mathrm{R}=92.90\%$ and $\mathrm{RMSE}=1.1494$ for testing), with an architecture composed by 6-30-1 (input variables, hidden neurons, and output value). Additionally, a sensitivity analysis identified the MLP and RBF as the techniques that best represent the physical behavior of the phenomenon studied. According to the sensitivity analysis, the most influential variables were the environmental temperature and outdoor relative humidity, followed by solar irradiation, working hours, wind speed, and the number of occupants. The proposed methodology represents an alternative method to simplify the analysis of thermal modeling in buildings exposed to tropical climates based on an experimental measurement approach. It can be applied for the development of smart sensors aimed at the efficient management of energy and thermal comfort in buildings.

This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.

An assessment of the thermal refurbishment of an outpatient medical center in a tropical location, such as the City of San Francisco de Campeche, was presented with the aim to diminish its energy consumption. A year-long energy audit of the facility was conducted to formulate and validate a numerical simulation model while scrutinizing enhancement strategies. The examined improvement alternatives encompass passive adjustments to the roof (utilizing insulating materials, applying reflective coatings, and installing a green roof), modifications to active systems incorporating inverter technology, and alterations to the walls via reflective paint. The outcomes of the simulated enhancement scenarios were assessed utilizing energy, environmental, and economic metrics: key performance index (KPI), equivalent CO₂ emission index (CEI), and net savings (NS). These results were subsequently juxtaposed against TOPSIS decision-making algorithms to ascertain the alternative that optimally balances the three options. It was identified that using reflective paint on the roof provides the best energy benefits and contributes to mitigating emissions from electricity use. Furthermore, combining this passive technology with the integration of inverter air conditioning systems offers the best economic return at the end of 15 years. For its part, the TOPSIS method indicated that by prioritizing the financial aspect, the reflective coating on the roof combined with inverter air conditioning is enough. However, adding a wall with insulating paint brings environmental and energy benefits. The results of this work serve as a starting point for the analysis of other post-occupied buildings in the region and others under tropical climatic conditions.

El enfoque convencional para alcanzar el confort térmico generalmente se centra en modificar la temperatura de consigna en edificios totalmente climatizados. Sin embargo, el enfoque del confort térmico adaptativo es una alternativa que considera la interacción entre los edificios, el clima local y los usuarios para permitir mejoras significativas en el ahorro de energía. El trabajo analiza la viabilidad de implementar estrategias de confort térmico adaptativo, comparando modelos adaptativos basados en la norma ASHRAE 55-2020 y un modelo regional para la tipología climática tropical de México, contrastando los resultados respecto del enfoque estático. Se analizó térmicamente 13 locaciones del Estado de Campeche obteniendo que las estrategias de ventilación son aplicables en todo el Estado y que ambos modelos aseguran mejoras en el consumo de energía. Además, los resultaron siguieren que es necesario desarrollar más modelos adaptativos locales para proponer estrategias con mejor potencial de impacto en la región.