Search

Renewable energy sources are constantly on the agenda because the fossil fuels used are limited and the need for energy is constantly increasing. Among these resources, solar energy stands out because it is clean and endless energy. Nowadays, heat energy and electrical energy production from solar energy are quite common. Photovoltaic (PV) solar panels can convert a limited portion of the solar energy falling on them into electrical energy. In PV panels, heat energy that cannot be converted into electricity is discharged back to the external environment. Photovoltaic thermal (PV/T) panels are used to remove this heat from the system and convert it into useful energy. Many cooling techniques are applied to reduce the surface temperature of PV/T panels and increase their electrical efficiency. One of these techniques is liquid-cooled PV/T panels. In some of the studies, forced circulation (using a pump) and in others natural circulation (thermosiphon effect) were applied. In this study, a natural circulation indirect heated PV/T system was designed. Al2O3, ZnO, and BN nanoparticle concentrations were added to the cooling water to increase heat transfer within the PV/T panel. According to the experimental results, using nanofluid in the PV/T panel increased the thermal and total efficiency. Total efficiencies of ZnO, BN, and Al2O3 were obtained as 52.8 %, 47.86 %, and 43.49 %, respectively, at 0.03 concentration. The highest exergy efficiency and sustainability index were determined as 17.155 % and 1.207, respectively, at 0.03 concentration of ZnO nanofluid.

The need for energy is rising significantly with the growth of technology in the world. This energy need is largely met by fossil fuels. The enhancement in their prices and the damage they induce to the environment, scientists have turned to alternative energy sources due to the depletion of fossil fuels. In recent years, these alternative energy sources have come to the fore as solar, wind, and wave energy. However, heating and refrigeration systems, whose share of energy consumption in buildings in the world is 40 %, can also compete with these alternative energy sources. In particular, heat pumps (HP) are at a level that can compete with renewable energy sources to seriously reduce this rate. In this study, different nanoparticles were added to the Polyol ester oil (POE) utilized in the compressor to enhance the performance of the HP. Thermodynamic, environmental, and economic performances of the obtained nanolubricants at different concentrations (0.5 wt% and 1 wt%) and flow rates (15, 30, and 45 g/s) were evaluated. The highest COP value of the HP was calculated as 4.14 at 0.5 wt% B-POE at 45 g/s. The best energy consumption in the HP was obtained with 0.5 wt% B-POE nanolubricant with a decrease of 10.96 % at 45 g/s compared to pure POE. The highest exergy efficiency in the HP was calculated at 0.5 wt% B-POE nanolubricant with a 13.53 % increase at 30 g/s compared to pure POE. The best exergoeconomic parameter (Rg,ex) performance was determined as 3.7148 kWh/$ in 1 wt% TiO2-POE nanolubricant at 45 g/s. The best enviro-economic value of 0.16182 ¢/h was obtained with 0.5 wt% B-POE nanolubricant at 45 g/s. In line with the results obtained, it was observed that the B-POE nanolubricant has a performance that can compete with the good-performing TiO2-POE nanolubricant.

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62 %, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminum oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58 %, 28.43 %, 13.45 %, and 8.54 % respectively. TOPSIS prioritized Graphene Oxide as the most favorable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminum-Oxide received the lowest priority in optimizing PV panel performance.

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

In this work, pebbles of higher specific heat than the conventional absorber materials like aluminium or copper are proposed as a absorber in the solar flat plate collector. The proposed collector are integrated into the building design and constructed with masonry. Tests were conducted by varying the operating parameters which influence its performance, like the flow rate of the heat-absorbing medium, and the tilt of the collector using both coated and uncoated pebbles. The maximum temperature difference that could be measured for a conventional absorber was approximately 8 °C for a flow rate of 0.6 L/min. While for a coated and uncoated absorber, it was 7 °C and 5.5 °C respectively. This difference decreased with an increase in flow rates from 0.6 L/min to 1.2 L/min. For all the flow rates, it was observed that the average difference in efficiency between the coated and the conventional absorber collector is 5.82 %, while the difference between the coated and uncoated absorber collector is 15.68 %. Thus, it is very much evident that by replacing the conventional absorber with the proposed coated pebble absorber, the overall loss in efficiency is just 5.82 %, but the advantages are enormous. Along with the experimental study, numerical analysis was also carried out with CFD modeling. The numerical results agreed well with experimental results with the least error. Therefore, CFD simulation can be further used to optimize the design of the collector.

Photovoltaic (PV) systems are capable of meeting the urgent demand for power production for both domestic and commercial purposes. PV systems possess serious drawbacks as their performance is heavily influenced by environmental variables like wind, radiation, shadow, dust, and soil accumulation. The current work examines the performance of solar PV panels in the presence of soil and dust at various tilt angles. A solar PV simulator was used, and experiments were conducted for a hot-dry climate location (Vellore, Tamil Nadu, India, 12.91° N, 79.1325° E), to evaluate the performance of solar PV panels under varying dust deposition. A total of seven different samples, such as black soil, desert soil, red soil, alluvial soil, laterite soil, coal dust, and bird droppings, were selected and dispersed over the surface of the PV panel at various weights of 10, 20, 30, 40, and 50 g. The physical characteristics of the dust samples have been emphasized as being essential in determining how effectively the PV panel functioned. Bird droppings were shown to have the greatest influence on PV panel efficiency because of their tendency to stick to the panel surface due to moisture content, but coal dust, independent of tilt angle, was found to have the least effect. Coal dust was determined to have the least impact of all soil types since it is quickly blown away and does not stick to the surface. Bird droppings accounted for about 46.42 %–89.18% of the efficiency loss, which was determined to be high, whereas coal dust accounted for less than 13% of the efficiency loss. Furthermore, it was revealed that considered tilt angles (0O and 12.91O) have a minor influence on the PV's overall performance.

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power.

Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radiation with low errors. Accordingly, it is noticed that various types of models have been continuously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accordingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.

The heat pumps are frequently used in domestic and industrial applications for hot water supply. The present paper aims to thermodynamically investigate the impacts of the nanoparticle-addition into the lubricants on the energetic, exergetic, and thermoeconomic aspects of a heat pump. In the experiments, air to the water heat pump is separately charged with various metal oxide-based nanoparticles (Al2O3, CuO, and TiO2)-added oils at a constant mass fraction of 0.5%. Polyolester (POE) and 134a are used as a lubricant, and refrigerant, respectively. The mass flow rates of the water passed through the condenser are varied from 10 to 25 g/s with an interval of 5 g/s. In the results, it is observed that the thermal conductivity value noteworthy increases with the presence of nanoparticles in POE. The highest increment in thermal conductivity is found to be 39% for POE + CuO in comparison with that of pure POE. Furthermore, with nanoparticles addition, it is noticed that the COP value generally improves, and the highest improvement for COP value is noticed to be 8% for POE + TiO2 nanolubricant at the mass flow of 25 g/s. Furthermore, exergy efficiency enhances by 3.6%, 1.8%, and 4.5% for POE + Al2O3, POE + CuO, and POE + TiO2, respectively. The lowest heating cost is calculated to be 3.465 ¢/kWh at 20 g/s flow rate for POE + Al2O3. In conclusion, this paper clearly reports that usage of nanoparticles along with lubricants is presenting better energetic, exergetic, and thermoeconomic results rather than the usage of lubricant alone in the heat pumps.

Energy consumption is the biggest obstacle in the economic growth of a country. In recent years, Turkey has imported around at the rate of three-quarters of its total energy demand. Upon the past 10-years running, Turkey paid nearly half a trillion dollars for its total energy bill. The big share of energy consumption has emerged from buildings. Therefore, energy savings have great importance, particularly in the buildings. A refrigerator is responsible for the most dominant electrical energy consumption rate with 32% in a house. Therefore, this paper proposes a novel household refrigerator design for reducing energy consumption. In the proposed design, the necessary air for the cooling process will be provided from outdoor ambient in appropriate weather condition. The compressor work will, thus, be decreased via this way, and contribute to a reduction in energy consumption. The results indicated that this system in 63 provinces can be effectively used between 1 and 4 months and help to reduce 36 million $ in Turkish electric energy bill with the use of only 1 year period. Additionally, a reduction of approximately 850,000 tons of CO2 annually in Turkey can be achieved by applying the proposed design in this study. Hereby, Turkey can contribute not only to be sustained economic growth but also to reduce harmful gas emissions arising from electricity generation methods in the country.

A significant portion of global energy consumption is due to energy consumption in the buildings. Heating, cooling, and air conditioning systems have the largest share in this energy consumption. Evaporative cooling systems, which have the advantage of being economical, zero pollution, and easy maintenance are preferred to reduce energy consumption in buildings. These systems are used in many areas such as greenhouses, broiler houses, and warehouses. In this study, analyzes of exergy, sustainability, and cooling efficiency in four different situations of a ground source direct evaporative cooling system were made. The system was studied in four different cases. While the highest exergy efficiency was obtained in case 3 with 20.83%, the exergy efficiencies in other cases were obtained as 16.83%, 17.49%, and 18.36%, respectively. In addition, the highest specific exergy loss was determined as 100.51 J/kg in case 2, while it was calculated as 73.08 J/kg, 80.23 J/kg, and 73.05 J/kg for the other cases, respectively. It is seen that the sustainability values are in parallel with the exergy efficiency when the evaporative cooling system is examined for four different cases. The sustainability values were determined as 1.20 for case 1, 1.21 for case 2, 1.26 for case 3, and 1.22 for case 4. It is determined that the exergy efficiency gives precise information about the usability and sustainability of the system when these situations are evaluated. The exergetic improvement potential (EIP) was determined as 0.061 for case 1, 0.082 for case 2, 0.063 for case 3, and 0.059 for case 4, respectively. Although the highest exergy efficiency is obtained in case 3, it has a higher recovery potential than case 1 and case 4. In addition, cooling efficiencies for four different cases were obtained as 33.70%, 34.81%, 41.69%, and 36.95%, respectively. The temperature differences between the room and ambient temperatures were determined as 1.45 °C, 1.21 °C, 1.6 °C, and 1.48 °C for each case, respectively.

Solar energy systems are widely utilized to obtain environmentally friendly and sustainable electrical and thermal energy that able to be used in many applications. Temperature-controlled solar air heater (SAH) system with a zigzag finned plate and flat plate was designed, manufactured, and tested experimentally in this study. It was determined that the set temperature was 15% higher than the flat plate SAH outlet temperature. The most important cause for this increase, the air is exposed preheating in the first collector. As the heat transfer surface area raised thanks to the zigzag fins in the second collector, the temperature of the air increases even more. SAH system's energy efficiency was found to be 71.15% on average. SAH system's maximum exergy efficiency was determined as 3.7%. The SAH system's average exergy destruction is calculated to be 651.58 W on average. According to the enviroeconomic analysis of the system, hourly CO2 mitigation was found to be 1.04 kg CO2/h and the environmental cost was 1.508 ¢/h. The energy cost was calculated as 0.0834 $/kWh, while the exergoeconomic parameter was calculated as 0.1931 kWh/$. In addition, the energy payback period was determined as 1.35 years, while the exergy payback period was determined as 45.9 years.