Electrification of powertrain systems offers numerous advantages in the global trend in vehicular applications. A wide range of energy sources and zero-emission propulsion in the tank to wheel significantly add to electric vehicles’ (EV) attractiveness. This paper presents analyses of the energy balance between micro-photovoltaic (PV) installation and small electric vehicle in real conditions. It is based on monitoring PV panel’s energy production and car electricity consumption. The methodology included energy data from real household PV installation (the most common renewable energy source in Poland), electric vehicle energy consumption during real driving conditions, and drivetrain operating parameters, all collected over a period of one year by indirect measuring. A correlation between energy produced by the micro-PV installation and small electric car energy consumption was described. In the Winter, small electric car energy consumption amounted to 14.9 kWh per 100 km and was 14%... [more]

Taking climate and geopolitical issues into account, we must shift our thinking towards “eco” and focus on renewable energy. The accessible solar energy represents 400 times the amount of consumption, while its potential represents 10,000 times the amount of demand. The paper aims to analyze recycled, customized polyvinyl butyral (PVB) with high purity (more than 98%) concerning its physicochemical and mechanical properties and its possible applicability in the photovoltaic industry as an encapsulating material. The detailed investigation on polyvinyl butyral starting from characterizations, homogenization, and moulding process to tensile tests and used exposure testing in laboratory apparatus are performed. Samples of recycled polyvinyl butyral were exposed to ultraviolet (UV) radiation of the value 0.76 W.m−2.nm−1 at 340 nm, water spray, drying at 50 °C and condensation for 320 h when the radiation was turned off. The results obtained were more controlled in a laboratory environment... [more]

Solar photovoltaic (PV) is playing a major role in the United Arab Emirates (UAE) smart grid infrastructure. However, one of the challenges facing PV-based energy systems is the dust accumulation on solar panels. Dust accumulation on solar panels results in a high degradation in the output power. The UAE has low intensity rainfall and wind velocity; therefore solar panels must be cleaned manually or using automated cleaning methods. Estimating dust accumulation on solar panels will increase the output power and reduce maintenance costs by initiating cleaning actions only when required. In this paper, the impact of natural dust accumulation on solar panels is investigated using field measurements and regression modeling. Experimental data were collected under various real weather conditions and controlled levels of dust. Moreover, this paper proposes a data-driven approach based on machine learning to estimate the accumulated dust level on solar panels. In this approach, a dust estimati... [more]

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50−0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using... [more]

Solar thermal power is nowadays one of the trendiest topics in the construction industry, and it represents a valuable energy source of heating that reduces energy consumption. As solar panels produce heating during the day and consumers demand calefaction during the whole day, we use standby tanks (for domestic hot water) and buffer tanks (for heating) for storage. The latest developments improved the efficiency and useful life while reducing the volume of tanks. So, the presented research work deals with analyzing the solar thermal power in a family house. This work presents a method to create a decision support system to compare solar energy systems in houses from economical, technical, availability, and environmental concerns. The weights of the criteria selected considering the analytical hierarchy process are computed. Parameters required for energy production calculations (location, temperature, etc.) and energy consumption (inhabitants, outdoor temperature, etc.) are summarized... [more]

Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways, such as mechanical pressure, this study presents the development and implementation of an innovative charging technique for electric vehicles (EVs) by fully utilizing the existing roadways and state-of-the-art nanotechnology and power electronics. The developed Smart Illuminative Charging is a novel wireless charging system that uses LEDs powered by piezoelectric materials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle’s battery. The piezoelectric materials were tested for their mechanical-to-electrical energy conversion capabilities and the relatively large-area EH2N samples (2 cm × 2 cm) produced high output voltages of up to 52 mV upon mechanical pressure. Furthermore, a lab-scale prototype device was developed to testify the proposed mechanism of i... [more]

This review uses a more holistic approach to provide comprehensive information and up-to-date knowledge on solar energy development in India and scientific and technological advancement. This review describes the types of solar photovoltaic (PV) systems, existing solar technologies, and the structure of PV systems. Substantial emphasis has been given to understanding the potential impacts of COVID-19 on the solar energy installed capacity. In addition, we evaluated the prospects of solar energy and the revival of growth in solar energy installation post-COVID-19. Further, we described the challenges caused by transitions and cloud enhancement on smaller and larger PV systems on the solar power amended grid-system. While the review is focused on evaluating the solar energy growth in India, we used a broader approach to compare the existing solar technologies available across the world. The need for recycling waste from solar energy systems has been emphasized. Improved PV cell efficienc... [more]

Ensemble deep learning methods have demonstrated significant improvements in forecasting the solar panel power generation using historical time-series data. Although many studies have used ensemble deep learning methods with various data partitioning strategies, most have only focused on improving the predictive methods by associating several different models or combining hyperparameters and interactions. In this study, we contend that we can enhance the precision of power generation forecasting by identifying a suitable data partition strategy and establishing the ideal number of partitions and subset sizes. Thus, we propose a feasibility study of the influence of data partition strategies on ensemble deep learning. We selected five time-series data partitioning strategies—window, shuffle, pyramid, vertical, and seasonal—that allow us to identify different characteristics and features in the time-series data. We conducted various experiments on two sources of solar panel datasets coll... [more]

Due to rising fossil fuel costs, electricity tariffs are also increasing. This is motivating users to install nanogrid systems to reduce their electricity bills using solar power. However, the two main constraints for a solar system installation are the initial financial investment cost and the availability of space for the installation of solar panels. Achieving greater electricity savings requires more panels and a larger energy storage system (ESS). However, a larger ESS also increases the electricity bill and reduces the available solar power due to higher charging power requirements. The increase in solar power leads to the need for more space for solar panel installation. Therefore, achieving the maximum electricity savings for a consumer unit requires an optimized number of solar panels and ESS size within the available financial budget and the available physical space. Thus, this study presents a fiscal- and space-constrained mixed-integer linear programming-based nanogrid syst... [more]

In the plan of the European Green Deal, the European Union assumed that by 2050 Europe will become the first climate-neutral continent in the world. This will be supported by alternative (renewable) energy sources (RESs), also termed green energy (GE). Their use should have long-term environmental benefits. To do this, it is necessary to skillfully select RES products. Therefore, the purpose is to develop a method for selecting devices that convert to GE, including not only qualitative criteria, but also environmental criteria and their price. The method is based on customer requirements and expert knowledge. The general concept of the method allows for the assessment of selected qualitative and environmental criteria of products and determining the price of purchase of these products. In a hybrid way, the following techniques were used: SMARTER method, brainstorming (BM), MAP method (alternative-punctual Czechowski’ method), ACJ method (price−qualitative analysis). Based on the result... [more]

Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The propo... [more]