This work studies the efficiency and long-term viability of powered hydrogen production. For this purpose, a detailed exploration of hydrogen production techniques has been undertaken, involving data collection, information authentication, data organization, and analysis. The efficiency trends, environmental impact, and hydrogen production costs in a landscape marked by limited data availability were investigated. The main contribution of this work is to reduce the existing data gap in the field of hydrogen production by compiling and summarizing dispersed data. The findings are expected to facilitate the decision-making process by considering regional variations, energy source availability, and the potential for technological advancements that may further enhance the economic viability of electrolysis. The results show that hydrogen production methods can be identified that do not cause significant harm to the environment. Photolysis stands out as the least serious offender, producing 0 kg of CO₂ per kg of H₂, while thermolysis emerges as the major contributor to emissions, with 20 kg of CO₂ per kg of H₂ produced.

Data Mining techniques have been applied to data collected from a 222 kWp CdTe (Cadmium Telluride) photovoltaic (PV) generator to predict faults or special conditions that occurs due to shadows, bad weather, soiling, and technical faults. Five types of errors have been distinguished and its impact on the PV system performance has been evaluated. Up to date, this computing approach has needed the simultaneous measurement of environmental attributes that an array of sensors collected. This study presents a model to assess the state of the PV (photovoltaic) generator and an algorithm that classifies its state without measuring ambient conditions. The result of a 222 kWp CdTe PV case study shows how the application of computing learning algorithms can be used to improve the management and performance of the photovoltaic generators and underlines the environmental parameters as clue attributes to find faults during the PV performance. Although the application of this method requires computational effort, the result deals with an easy-implementing decision tree, which can be installed in small device.

Given the current need to reduce the Urban Heat Island (UHI) worldwide, one of the strategies that can contribute to this mitigation is green façades. In this context, the aim of this research is to evaluate a Living Wall System (LWS) as a strategy to reduce the urban heat island in Santo Domingo, Dominican Republic, using outdoor test cells. This research was focused on the monitoring of two different façades, an LWS and a reference façade, during the warmer months. For the comparison, the parameters measured were air temperature, relative humidity, surface temperature and environmental variables. In addition, thermal images were taken. Results reveal that during the days selected, the average outdoor air temperature difference between the LWS compared to the reference façade was 5.3 °C, whereas during the day, the average was 3.3 °C. Concerning surface temperature, in the case of the LWS, the temperature was higher and had greater fluctuations than the reference façade. This behavior was confirmed by the results obtained with thermal images. In conclusion, using an LWS in a tropical climate helps the urban microclimate, which contributes to urban heat island effect mitigation during the warmer months.

Access to electricity has a positive impact on the socio-economic activity in rural livelihoods. Stand-alone photovoltaic (SAPV) systems are the most popular PV application for rural electrification in areas nonconnected to the grid, but it is still relatively expensive. Batteries are considered as a weak component of the system, comprising an important part of the total cost and are usually replaced multiple times during PV system lifetime. A priority load control algorithm has been developed in order to gain a better energy management over system loads and the battery storage, and therefore guarantee the energy supply for critical loads and extends the battery service lifetime. This will increase the reliability of the system and the end-user satisfaction. This article describes a stand-alone PV system model used for the development of a priority load control algorithm and explains and implements the algorithm. The results of several test scenario simulations are shown and discussed.