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.
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.
The high cost of energy consumption in buildings highlights the importance of research focused on improving the energy efficiency of building’s envelope systems. It is important to characterize the real behavior of these systems to know the effectiveness in terms of energy reduction. Therefore, the aim of this paper is to characterize the thermal performance of facades based on experimental monitoring of outdoor test cells in tropical climate. To carry out this research, a case study was presented to compare two construction systems. One of them is a light façade (M1) and the other a reference façade (M2). A thermal simulation was performed for the opaque and glazed facades. In addition, several parameters were measured with different types of sensors, as well as environmental variables to evaluate the thermal and lighting behavior of multiple facades systems under real conditions. The findings show that light façade behavior was the opposite of what was expected, since by incorporating a window in the façade it has allowed solar radiation to increase the interior temperature in both modules. In the case of the light facade the penalization was higher than the reference facade, which has a lower thermal transmittance than M1. Doi: 10.28991/cej-2021-03091773 Full Text: PDF
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.