Search Results (273)

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

This study presents a comprehensive analysis of the long-term energy performance and economic viability of offshore wind farms planned for locations within the Polish Exclusive Economic Zone of the Baltic Sea. It focuses on the impact of wind farm layout, aerodynamic wake effects, and rotor blade surface degradation. Using the Jensen wake model, modified Weibull wind speed distributions are computed for various turbine spacing configurations (5D, 8D, and 10D) and wake decay constants $k_w\in \{0.02;0.03;0.05\}$. The results reveal a trade-off between turbine density and individual turbine efficiency: tighter spacing increases the total annual energy production (AEP) but also intensifies wake-induced losses. The study shows that cumulative losses due to wake effects can range from 16.5% to 38%, depending on the scenario considered. This corresponds to capacity factors ranging from 33.4% to 45.2%. Finally, lifetime productivity scenarios over 20 and 25 years are analyzed, and the levelized cost of electricity (LCOE) is calculated to assess the economic implications of design choices. The analysis reveals that, depending on the values of the considered parameters, the LCOE can range from USD 116.3 to 175.7 per MWh produced. The study highlights the importance of early stage optimization in maximizing both the energy yield and cost-efficiency in offshore wind farm developments. Full article

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article

Currently, Poland is witnessing a dynamic development of the offshore wind energy sector, which will be a key component of the national energy mix. While many international studies have addressed wind energy deployment, there is a lack of research that compares the energy and economic performance of both onshore and offshore wind farms under Polish climatic and spatial conditions, especially in relation to turbine spacing optimization. This study addresses that gap by performing a computer-based simulation analysis of three onshore spacing variants (3D, 4D, 5D) and four offshore variants (5D, 6D, 7D, 9D), located in central Poland (Stęszew, Okonek, Gostyń) and the Baltic Sea, respectively. The efficiency of wind farms was assessed in both energy and economic terms, using WAsP Bundle software and standard profitability evaluation metrics (NPV, MNPV, IRR). The results show that the highest NPV and MNPV values among onshore configurations were obtained for the 3D spacing variant, where the energy yield leads to nearly double the annual revenue compared to the 5D variant. IRR values indicate project profitability, averaging 14.5% for onshore and 11.9% for offshore wind farms. Offshore turbines demonstrated higher capacity factors (36–53%) compared to onshore (28–39%), with 4–7 times higher annual energy output. The study provides new insight into wind farm layout optimization under Polish conditions and supports spatial planning and investment decision making in line with national energy policy goals. Full article

Analyzing the soil organic carbon (SOC) stock in dryland areas of southern Shanxi, particularly under the influence of fertilization and mulching conditions, is crucial for enhancing soil fertility and crop productivity and understanding the SOC pool’s resilience to future climate change scenarios in the region. In a long-term experimental site located in Hongtong County, Shanxi Province, soil samples were collected from the 0–100 cm depth over a nine-year period. These samples were analyzed to evaluate the impact of five treatments: no fertilization and no mulching (CK), conventional farming practices (FP), nitrogen reduction and controlled fertilization (MF), nitrogen reduction and controlled fertilization with ridge-film furrow-sowing (RF), and nitrogen reduction and controlled fertilization with flat-film hole-sowing (FH). The average annual yield of wheat grain, SOC stock, water-soluble organic carbon (WSOC), particulate organic carbon (POC), light fraction organic carbon (LFOC), mineral-associated organic carbon (MOC), and heavy fraction organic carbon (HFOC) stocks were measured. The results revealed that the FH treatment not only significantly increased wheat grain yield but also significantly elevated the SOC stock by 23.71% at the 0–100 cm depth compared to CK. Furthermore, this treatment significantly enhanced the POC, LFOC, and MOC stocks by 106.43–292.98%, 36.93–158.73%, and 17.83–81.55%, respectively, within 0–80 cm. However, it also significantly decreased the WSOC stock by 34.32–42.81% within the same soil layer and the HFOC stock by 72.05–101.51% between the 20 and 100 cm depth. Notably, the SOC stock at the 0–100 cm depth was primarily influenced by the HFOC. Utilizing the DNDC (denitrification–decomposition) model, we found that future temperature increases are detrimental to SOC sequestration in dryland areas, whereas reduced rainfall is beneficial. The simulation results indicated that in a warmer climate, a 2 °C temperature increase would result in a SOC stock decrease of 0.77 to 1.01 t·ha⁻¹ compared to a 1 °C increase scenario. Conversely, under conditions of reduced precipitation, a 20% rainfall reduction would lead to a SOC stock increase of 1.53% to 3.42% compared to a 10% decrease scenario. In conclusion, the nitrogen reduction and controlled fertilization with flat-film hole-sowing (FH) treatment emerged as the most effective practice for increasing SOC sequestration in dryland areas by enhancing the HFOC stock. This treatment also fortified the SOC pool’s capacity to withstand future climate change, thereby serving as the optimal approach for concurrently enhancing production and fertility in this region. Full article

This study aims to characterize the emissions of ammonia (NH₃) and methane (CH₄) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental management. The target barn was selected at a commercial dairy farm in Ulanchab, Inner Mongolia, China. Environmental factors, including temperature, humidity, wind speed, and concentrations of NH₃, CH₄, and CO₂, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO₂ mass balance method. Additionally, NH₃ and CH₄ emission prediction models were developed using the adaptive neural fuzzy inference system (ANFIS). Correlation analyses were conducted to clarify the intrinsic links between environmental factors and NH₃ and CH₄ emissions, as well as the degree of influence of each factor on gas emissions. The ANFIS model with a Gaussian membership function (gaussmf) achieved the highest performance in predicting NH₃ emissions (R² = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH₄ emissions (R² = 0.8977). The improved ANFIS model outperformed common models, such as multilayer perceptron (MLP) and radial basis function (RBF). This study revealed the significant effects of environmental factors on NH₃ and CH₄ emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions. Full article

An accurate estimation of future wind conditions is essential for calculating the annual energy produced by a wind farm. This estimation should be based on historical wind data collected over several years at the site location. However, research articles often rely on data grouped into 12 sectors. This article examines five methods to improve the speed and accuracy in the use of wind data. First, it studies the effect of inadequate Weibull parameter calculation based on historical data showing that purely mathematical fitting methods (the traditional ones) are not valid. Then, the error introduced by wind speed discretization is evaluated showing that the traditional binning of 1 m/s is not always the best choice. Next, the effect of using symmetric wind roses is examined, demonstrating that it is possible to reduce computation time by half for layouts exhibiting point symmetry, with negligible error for other layouts. After that, the effect of abrupt wind condition distributions caused by sectorization, which can alter results when searching for optimal configurations, is analyzed proposing continuous interpolation of wind data to improve result consistency. Finally, an alternative to the wind rose is proposed to provide a quick assessment of the highest-quality wind directions. Full article

Long-term shutdowns caused by ice formation on wind turbine blades can lead to significant power generation losses, a persistent issue for wind farm operators. The rapid acquisition of ice mass and thickness on blades under actual meteorological conditions can facilitate the more effective adjustment of operation and maintenance strategies, enabling the selection of appropriate de-icing methods and optimal human resource allocation. This study proposes a novel approach utilizing icing simulation data across various meteorological parameters to train a Multilayer Perceptron (MLP) neural network, enabling rapid ice accretion prediction while maintaining acceptable accuracy. The results demonstrate that the MLP model achieves mean absolute percentage errors (MAPEs) of 7.13% and 7.02% for predicting rime ice mass and maximum thickness, respectively. For glaze ice prediction, the model yields MAPE values of 10.22% and 9.42% for ice mass and maximum thickness prediction, respectively. All MLP models exhibit R² values exceeding 0.95, indicating excellent model fitting. The model is used to simulate and analyze the blade icing condition of a wind farm (located at 27° N and 117° E). The results showed that during a typical icing cycle, the maximum hourly ice accumulation mass on the studied blade was 5.01 kg, and the accumulated ice accumulation mass over 24 h was 95.43 kg. The maximum hourly ice accumulation thickness was 10.38 mm, and the accumulated ice accumulation thickness over 24 h was 228.43 mm. Full article

This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWh at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure. Full article

Clam farms are typically located in remote areas with limited computational resources, making it challenging to deploy traditional deep learning-based object detection methods due to their large model size and high computational demands. To address this issue, this paper proposes a lightweight detection method, YOLOv8-RFD, based on an improved YOLOv8 algorithm, tailored for clam sorting applications. The proposed enhancements include the following: replacing the original backbone network of YOLOv8 with a Reversible Columnar Network (RevColNet) to reduce feature redundancy and computational load; upgrading the C2f modules in both the backbone and neck networks to C2f-Faster to optimize feature fusion strategies and improve fusion efficiency; and incorporating a Dynamic Head (DyHead) to enhance feature extraction and detection accuracy by adaptively adjusting the detection head structure. Experimental results on a custom clam dataset demonstrate that, compared to the original YOLOv8 model, the proposed method reduces the number of parameters by 22.75% and computational demand by 18.52%, while slightly improving detection accuracy. These improvements not only maintain but also enhance detection performance, significantly reducing computational cost, and confirming the method’s suitability for deployment in resource-constrained environments. This provides a reliable technical foundation for the sorting of clams. Full article

Dilemmas related to the development of demand for renewable energy encourage continuous evaluation of such investments in various locations, taking into account market and environmental conditions. The conducted study concerns the analysis of the profitability of investment in a 1.65 MW wind turbine with a hub height of 70 m in various zones in Poland. The analysis was performed using the clustering method (cluster analysis and the Czekanowski diagram). Computer simulation was also used using the Hybrid Optimization of Multiple Energy Resources (HOMER), ver. x64 3.18.4 software. As a result, three zones were distinguished that ensure differentiation in the rates of return on investment in wind energy. The authors positively verified the hypothesis about the spatial differentiation of profitability in relation to the examined factors. The justification for investments in wind farms was demonstrated and factors determining their profitability were indicated. It was emphasized that, in the case of wind farms, energy production is relatively predictable, which shapes the benefits for investors, and facilitates financial planning and long-term return on investment. Full article

The buffalo species (Bubalus bubalis) is crucial for the global economy, supplying high-nutritional-value animal proteins vital for children’s growth. These animals efficiently convert fiber into energy and thrive in various harsh environments, from frigid climates to hot, humid areas, including wetlands. They produce milk and meat while supporting the sustainability of ecosystems that other ruminants cannot inhabit. Buffalo offers a unique opportunity to supply resources for both rural communities and larger farms located in specific regions, such as marshlands and humid savannahs. They also thrive on extensive pastures and family farms, thus preserving biodiversity, habitats, and cultural practices. Intensive farming brings distinct challenges and is often criticized for its negative effects on climate change. To counter these impacts, multiple strategies have been researched and implemented. These include enhancing livestock genetics, adopting sustainable agricultural practices, optimizing local feed resources (including by-products), managing manure (with an emphasis on renewable energy), and improving animal health and welfare. This review explores various buffalo farming system applications in different global contexts. It is based on the hypothesis that the adaptable traits of buffalo, as well as the environmental and economic challenges that must be addressed for sustainability, are the key factors in determining the viability of such enterprises. Full article

With economic development and improved living standards, the demand for mutton and wool continues to grow, and improving the production performance and genetic potential of sheep breeds has become the key to promoting the high-quality development of the sheep industry. Thus, this study analyzes the influencing factors of the early production traits of Yunnan semi-fine wool sheep, optimizes the genetic evaluation model, and relies on accurate genetic parameter estimation to provide a theoretical basis for formulating a scientific and efficient breeding strategy for this breed. Data were collected from the Laishishan and Xiaohai breeding farms in Qiaojia, Yunnan, covering production records of the core flock from 2018 to 2022. Using the GLM procedure in SAS 9.4 software, this study analyzed the non-genetic influences on early production traits in Yunnan semi-fine wool sheep. Concurrently, Danish Milk Unit 5 (DMU 5) software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving the genetic parameters. The results show that the birth year, dam age, sex, flock and litter size significantly affect both the Birth Weight (BWT) and Weaning Weight (WWT) (p < 0.01). Additionally, the birth month was found to exert a significant effect on Birth Weight (BWT) (p < 0.01), the weaning month has a significant effect on the Weaning Weight (WWT) (p < 0.05). No significant effects of farm location were observed on either trait (p > 0.05). The most accurate genetic evaluation model determined the heritability of the Birth Weight (BWT) and Weaning Weight (WWT) as 0.3123 and 0.3471. From a production perspective, improving lamb birth, Weaning Weight (WWT), feed composition, and maternal nutrition during gestation is vital for breeding efficiency. This study not only identified the optimal animal models for early growth traits in Yunnan semi-fine wool sheep, offering a precise basis for estimating genetic parameters but also provides theoretical guidance for genetic selection and breed improvement in this population. Full article

In recent years, the utilization of renewable energy sources has significantly increased due to their environmentally friendly nature and sustainability. Among these sources, wind energy plays a critical role, and accurately forecasting wind power with minimal error is essential for optimizing the efficiency and profitability of wind power plants. This study analyzes hourly wind speed data from 23 meteorological stations located in Türkiye’s Western Black Sea Region for the years 2020–2024, using the Weibull distribution to estimate annual energy production. Additionally, the same data were forecasted using the Long Short-Term Memory (LSTM) model. The predicted data were also assessed through Weibull distribution analysis to evaluate the energy potential of each station. A comparative analysis was then conducted between the Weibull distribution results of the measured and forecast datasets. Based on the annual energy production estimates derived from both datasets, the revenues, costs, and profits of 10 MW wind farms at each location were examined. The findings indicate that the highest revenues and unit electricity profits were observed at the Zonguldak South, Sinop İnceburun, and Bartın South stations. According to the LSTM-based forecasts for 2025, investment in wind energy projects is considered feasible at the Sinop İnceburun, Bartın South, Zonguldak South, İnebolu, Cide North, Gebze Köşkburnu, and Amasra stations. Full article

The reduction of harmful emissions is shaping trends across many industries, including architecture and building. With rising ecological awareness and the threat of climate change, architects, construction engineers, and developers are focusing on innovative solutions to minimize the construction sector’s environmental impact. This paper presents a technical and management approach system using renewable energy sources, based on an existing single-family house with known energy consumption. The aim is to achieve energy independence by relying solely on on-site electricity generation and storage, while remaining connected to water and sewage infrastructure. Utilizing renewable energy sources enhances self-sufficiency and investment profitability. The study evaluates the house’s energy consumption to optimally select electricity supply solutions, including a small wind farm and photovoltaic installation integrated with appropriate electricity storage. This is crucial due to the air heat pump used for heating and domestic hot water, which requires electricity. An hourly simulation of the system’s operation over a year verified the adequacy of the selected devices. Additionally, two different locations were analyzed to assess how varying climate and wind conditions influence the design and performance of off-grid energy systems. The analysis showed that solar and wind systems can meet annual energy demand, but limited storage capacity prevents full autonomy. Replacing the heat pump with a biomass boiler reduces electricity use by about 25% and battery needs by 40%, though seasonal energy surpluses remain a challenge. This concept aligns with the goal of achieving climate neutrality by 2050. Full article