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The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Electric car charging stations are in high demand as a result of the development of the e-mobility sector and the adoption of electric vehicles in transportation. This study aims to construct and analyze a stand-alone solar PV-powered electric car charging station to fulfil electric vehicle load demand and make recommendations for optimizing its operation. The goal is to achieve 3D’s i.e., Decarbonization, Digitalization and Decentralization in both the transport and power supply (electricity supply). Advancing towards attaining 3D’s goal, an off-grid solar PV-powered EV charging station was built at the University of Sharjah to meet the load demand. The EV charging station includes PV panels, inverters, energy storage devices and EV charging outlets. A solar PV system of 7.4 kWp with an energy storage capacity of 34.56 kWh is installed. A battery inverter of 4.6 kW is incorporated to meet the load demand of three electrical vehicles simultaneously. The performance of the system is monitored over a year. The EV charging station is in off-grid mode; thus, it is following the load demand for PV energy production. The findings reveal that the total energy production by the PV system is 2971.59 kWh from which the direct consumption is 383.77 kWh (12.91 %), and 2587.83 kWh (87.09 %) is stored in the energy storage devices. The total energy consumption by electric vehicles is 2379.01 kWh of which 1995.24 kWh (83.87 %) is taken from the battery bank. The PV system is working at 24.58 % of its full capacity. By elevating the daily load demand to 33 kWh, the full utilization of the solar resource can be achieved. To evaluate the adverse effect of dust, natural dust is allowed to settle down on the surface of PV panels continuously for 322 days. A linear degradation was recorded in the performance ratio from 77.92 % to 27.30 % in 218 days. Thereafter a plateau was attained and insignificant variation (min. 26.36 % and max. 33.78 %) was observed in the next 104 days. It is concluded that once a thick layer of dust forms on the surface of the PV panels, further dust accumulation becomes minimal.

Surfaces such as pavement, buildings, and other heat-absorbing surfaces that store more heat than natural vegetation generate the Urban Heat Island (UHI) effect. Temperatures are known to be significantly higher in urbanized areas compared to rural ones.The application of a reflective surface treatment to the pavement improves its solar reflectivity while lowering the warmth of its surface. The main objective of this study is to assess the performance of a cool pavement in an educational complex parking area, under desert climatic conditions to reduce heat islands. White and high reflective material was applied to the tested parking pavement area. Various experimental techniques were used to assess the performance of non treated (baseline) and treated (cool) pavements including spectral radiation, reflectance (albedo), solar irradiance, and pavement surface temperature. The results show that the coated pavement has a 15 % reflectance in ultraviolet A (UVA) and the untreated pavement has a 17 % solar reflectance (baseline basement). There are no potential dangers to human health posed by the coated pavement's reflection of UVA (For visible wavelengths, coated pavement has a reflectance of roughly 70 %, while uncoated pavement has a reflectivity of only 24 %. Under desert climatic conditions, the results show a net decrease in surface temperature of the coated pavement by up to 15 °C compared to the uncoated one. In the summer, for example, raising the albedo of the pavement surface from 24 % to 70 % lowered the surface temperature from 60 °C to 47 °C. This represents a 22 % decrease in pavement surface temperature. The thermal performance analysis of the cool pavement technology reveals that the pavement surface albedo enhancement will help to increase surface reflectance while lowering pavement surface temperature and offsetting CO2 emissions. Increasing the pavement surface albedo by 192 % (from 24 % to 70 %) for the total treated surface area (81,000 m2) in the educational complex will offset 28,350 tons of CO2. The application of a white coating with a high reflective value on concrete and asphalt pavements will help to extend the life of the pavement, reduce the energy consumption of buildings and the emissions of greenhouse gases, minimize the emissions of carbon dioxide caused by the enhancement of albedo (a strategy for mitigating the effects of climate change), improve parking lot visibility, and deal with the effects of heat islands.

The development of innovative sustainable methods and strategies for the conversion of biomass and waste materials to clean and alternative fuels using renewable power systems are needed to foster the clean energy transition. The main objective of this study is to optimize the performance of the pyrolysis reactor powered with solar parabolic trough collector (PTC) to maximize the quantity and quality of the biofuels production. The goal is to develop sustainable thermal conversion processes for the conversion of biomass and solid waste to renewable fuels. An integrated Modeling and simulation analysis based on the mass and energy balance of the thermal conversion process of the biomass (wood waste) and response surface methodology (RMS) for the optimization of the biofuel (bio-oil) yields was used in the present study. The effects of three main input factors: biomass volumetric flow rate, solar irradiance and biomass particle diameter on the performance of the PTC pyrolysis reactor were determined. The results show an average monthly bio-oil production of 44.58% by weight. The maximum bio-oil yield through the year occurs in May and June and it is around 58% by weight. Three new correlations for biofuel yields based on the three input factors were developed. The coefficients of determination R2 for the three correlations were between 0.8951 and 0.9986 (0.14–10.49% errors). The perturbation graph of bio-oil conversion yield shows that the most significant input factor is the solar irradiance followed by biomass volumetric flow rate and then the particle diameter. The optimum conditions for bio-oil production (volume flow rate = 0.0058 m3/h; solar irradiance = 918 W/m2, and biomass particle diameter of 8 mm) for the selected pyrolysis reactor are also determined. The results obtained in the course of this study show the benefits of using solar thermal energy for the conversion of biomass and solid waste to renewable and alternative fuels.

A statistical model for predicting pavement surface temperature has been developed for the first time in this study. The main objective of this work is to analyse the impact of the input factors (solar radiation, ambient temperature, wind speed, and albedo) most affecting the conventional rigid pavement surface temperature. A multi-physics numerical model combining radiative, conductive, and convective heat transfers with a response surface methodology (RSM) model was created as an innovative and integrated approach. The numerical model of two-dimensional heat transport based on volume finite techniques is performed using FORTRAN software. Moreover, the analysis of variance (ANOVA) is used to establish the polynomial model or new correlation (quadratic regression) and analyse each input factor's statistical importance. Heat transfer model findings were in good agreement with the experimental data. For pavement surface temperature, the results show that the adjusted Radj2and predicted determination coefficients R2 achieved are 0.9912 and 0.9604, respectively. A good agreement is obtained between the statistical model's prediction values and the heat transfer model's numerical data. Based on the four analysed input factors, a simple polynomial statistical model is developed to forecast and reduce the conventional rigid surface pavement temperature. A wind speed of 1.215 m/s, an air temperature of 293.634 K, solar radiation of 222.305 W/m2 and albedo of 0.782 were found to be the optimal operating conditions for obtaining the lowest possible temperature on the pavement's surface.

This work proposes an innovative statistical model by utilizing the response surface methodology (RSM) method to analyze a batch solar behaviour still. This investigation's main goal is to study the impact of the input factors (solar radiation, ambient temperature, water depth, and thickness of insulation) most influencing water productivity. The polynomial regression model derived from a numerical balance energy model to predict the solar's productivity still is established. The quadratic model is checked by a coefficient of determination (R2). An excellent fitting is attained between the forecasted results derived from the statistical model and the numerical simulation derived from the heat balance model. The results reveal that the importance of the influence in the order of impact on the amount of distilled water is water depth, solar radiation, ambient temperature, and thickness of insulation. A simple polynomial statistical model is stated in this investigation to determine and maximize the amount of distilled water from solar still based on the four considered input factors.

Gulf countries like the UAE are blessed with plenty of sunshine over the whole year. The solar photovoltaic system is an appropriate alternative for the production of electricity. Solar irradiance is partially converted into electricity and the remaining is dissipated as accumulated heat which elevates the operating temperature of the solar PV panel. The elevated temperature has adverse effect on the electrical performance of SPV panels. In this study, a unique back cooling, through the application of waste air from a centralized air conditioning system, is presented to compensate for the degradation of electrical power by the temperature. The experimental results have shown an average reduction of 18.05°C in the operating temperature of the examined monocrystalline solar panel, which improves the generated electrical power by8.65%. In a typical clear day of July, an improvement of 0.97%in the electrical efficiency of the SPV panel has been achieved through the proposed cooling strategy. The electrical energy produced by the cooled SPV panel is 677.89 Wh/day, as compared to 623.90 Wh/day of the reference panel. The results have justified the viability of waste air from the air conditioning system as a novel application of back cooling of SPV panels. The IR thermography is also employed to study the distribution of temperature and the estimation of the performance of SPV panels, along with the detection of the superficial defects. In the end, a new correlation is developed to accurately estimate the output power of the PV panel through IR measured temperature.

In the high concentrator photovoltaic (HCPV) systems with solar concentration ratios up to 2000 Suns, significant heat is generated in the used solar cell layer. This high generated heat requires an efficient and smart cooling technique to keep it operating at a safe operating temperature. In this paper, another ultra-high concentrator photovoltaic (UHCPV) system with a smaller cell area of 1 mm2 operating at a high solar concentration ratio (CR) up to 10,000 Suns is proposed. This smaller area requires a simple passive cooling technique even at high CR. The optimal dimensions of a passive cooling method using heat spreader are defined. A 3D thermal model for the multijunction solar cell with the heat spreader coupled with the multi-objective genetic optimization algorithm is used to define the optimal heat spreader dimensions . The model is validated with the results in the literature. The model is used to estimate the cell temperature generated electric power, and cell efficiency at different wind speed, ambient temperature, solar radiation, heat spreader length, thickness, and CR. The heat spreader dimensions were optimized for CR = 6000 suns, the optimal thickness and length were 2 mm and a of 47.5 mm, respectively. These dimensions are enough for the safe operation of the UHCPV at CR of 6000 Suns. As a case study, for a UHCPV module with a total number of cells of 10 by 10, the generated power is around 319 W at CR of 10,000 Suns. At the same condition, the monocrystalline silicon solar cell in the PERSEID SOLAR company can generate a maximum power of 144.9 W/m2. For the same area, for the UHCPV module, the generated electric power is around 319 W for 1 m2 of the module. Therefore, around 120% increase in the power can be accomplished with the use of the UHCPV module. In the UHCPV module, the total area of the cell is around 1 cm by 1 cm. Therefore, the module cost could be very low.

Cooling PV cells is an important requirement to prevent any loss performance of PV cell and any reduction of their lifespan. Therefore, using nanofluid as working fluid in a PVT collector has become attractive due to the significant improvement in heat transfer properties as compared to conventional fluids. For the first time, this work introduces a novel statistical model by applying the response surface methodology (RSM) method to examine the electrical and thermal efficiencies of behavior of a nanofluid photovoltaic thermal (PVT) collector. The developed statistical model correlates the efficiencies of the solar collector with the operational parameters, including the heat transfer coefficient by conduction between the PV module and the absorber plate, the mass flow rate, the concentration of Zinc oxide (ZnO) nanoparticles and the tubes number. An excellent correlation is achieved between the foretold results derived from the statistical model and numerical simulation of the heat transfer model. The coefficient of determination (R2) for thermal and electrical efficiencies are 0.9888 and 0.9908, respectively. The outcomes show that the heat transfer coefficient by conduction between the PV and the absorber, the mass flow rate are notable factors compared to concentration of ZnO nanoparticles and the tube number.

The performance of solar air conditioning system (SACS) using Lithium-Bromide/water (Li–Br/H2O) solution and nanofluid -based parabolic trough collectors (PTC) under hot and humid climate conditions is presented in this study. The main objectives are to investigate the effects of different types of nanofluid (Al2O3/H2O and Cu/H2O) and the concentration of nanoparticle Cu (0.2 and 0.5 wt %) on the coefficient of performance (COP) of SACS system during a winter and summer days in desert regions. Mathematical models of the SACS system including the solar PTC, tank storage and the single-effect Li–Br/H2O absorption chiller are developed. The numerical simulation results show a good agreement with the experiment data accessible in literature. The results show that Cu/H2O nanofluid has the best performance. For the cold day and at noontime, the COP for pure water, 0.5 vol% Al2O3/H2O nanofluid and 0.5 vol% Cu/H2O nanofluid are 0.64, 0.68, and 0.74, respectively. The COP during the cold day increases by 12.5% and 15.5% when the concentration of Cu nanoparticle is 0.2 %vol and 0.5 %vol respectively. For the hot day and between 9:00 a.m. to 3:00 p.m. the maximum COP obtained is 0.77, and the effect of nanofluids is negligible. The single LiBr–H2O air absorption system reaches its maximum operation. A double effect of LiBr–H2O air absorption is needed to improve the performance of the system. Keywords: Solar, Absorption chiller, Li–Br/H2O, Single effect nanofluid