This work proposes a new simplified five-parameter estimation method for a single-diode model of photovoltaic panels. The method, based on an iterative algorithm, is able to estimate the parameter of the electrical single-diode model from the panel’s datasheet. Two iterative steps are used to estimate the five parameters starting from data provided by the manufacturer (nameplate values or I–V curves). The first step permits finding the optimal value of the diode ideality factor A, and the second step allows the calculation of the R_p value to improve the accuracy. A model that takes into account variations in temperature and solar irradiance has been used to validate the behavior of the output parameters. Compared to other estimation work, the proposed method shows the best result in the standard test condition (STC) and with a variable solar irradiance. Indeed, the optimization of the A, R_s, and R_p parameters allows guaranteeing the minimum error between I–V curves obtained from method and datasheet.

In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving.

The main objective of this review is to summarize and thoroughly investigate the most popular and promising BIM (building information modeling) and BEM (building energy modeling) interoperability strategies employed in the last years (2004–2023), highlighting pros and cons of each strategy and trying to understand the reason for the still limited BIM–BEM interaction. This review summarizes the main countries, areas, modeling tools, and interoperability strategies, with the advantages and disadvantages of each one. The methodology is based on the PRISMA protocol, and two databases were used for the research: Scopus and Google Scholar. A total of 532 publications were selected and 100 papers were deemed useful for the purposes of this review. The main findings led to the identification of four different interoperability strategies between BIM and BEM tools: (1) real-time connection; (2) standardized exchange formats and middleware corrective tools; (3) adherence to model view definitions; (4) proprietary tool-chain. These strategies were found to be characterized by different degrees of complexity, time required for information exchange, proprietary or opensource software, ability to reduce information loss, and detailed energy results. The results of this study showed that, to date, there is no better interoperability strategy, and that further efforts are needed so that interoperability between the two tools can become commonplace.