The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

Near-Zero-Energy Buildings are a challenge in terms of energy production, storage, consumption and management, but these technological solutions remain financially difficult to access in developing countries. To this end, a complete low-cost and reliable home energy-management prototype was first developed and implemented on a scale model. A PWM charge controller drove the flow of energy produced and consumed in order to ensure the optimization of both the consumption of energy and energy savings. Battery storage was also managed by the home automation module using a set of sensors. The prototype of the scale model incorporated complete energy management of all electrical devices with group priorities through a graphical interface in a real-time mode. After testing this system, the lighting management part was implemented in a large-scale smart solar home. A smart lighting system via a complete algorithm integrated on an Arduino Mega board was then realized and implemented in the life-size house. This first step focuses, above all, on the users’ comfort and, in particular, on the lighting management. The results show that this smart device thus makes it possible to achieve additional energy savings on an essential and yet already energy-efficient device: lighting.

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R²). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R² values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

Digital stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the per sonnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope’ s diaphragm to objectively diagnose chronic obstructive pulmonary d isease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, anterior left (Al), anterior right (Ar), lateral left (Ll), lateral right (Lr), posterior left (Pl), posterior right (Pr) and trachea (Tc), were analyzed in this study. COPD+ and COPD - at diagnosis, each chest position was done objectively using mel frequency cepstral coefficients (MFCC) features and machine learning models namely support vector machine (SVM) and decision tree (DT). The res ults indicate that the Pr chest position offers superior precision, recall, and F1 - score, with a recognition accuracy of 99.7% in COPD screening.

Accurate control of the operating temperature is essential for achieving optimal efficiency in the use of solar panels. The Normal Operating Cell Temperature (NOCT) is a widely used method for estimating module temperature, but it is not always accurate for all conditions. In this paper, the authors propose four different models for estimating module temperature using various techniques including Neural Networks, a linear method proposed by Ross, and a Fitting method. The objective is to find more accurate methods than the NOCT model. The results show that all four models provide good agreement between measured and calculated module temperatures, with R² > 0.91 and RMSE < 3.74 for clear days, and considering the resources of time, cost, and expertise necessary for simulating or evaluating the proposed model, the two models based on RN neural networks offers a more cost-effective solution, by utilizing straightforward mathematical skills, the RN model demonstrates applicability in any environment, making it a preferable choice over the NOCT model for predicting polycrystalline photovoltaic module temperature under different climatic conditions. The proposed models can be used to estimate PV module temperature with good precision under different climatic conditions.