Search

To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. The objective is to identify the optimal interpolation method and apply it to wind energy assessments in the South China Sea. This paper compares the interpolation effects and accuracy of Linear, Cubic, and Bicubic interpolation methods on wind speed data, with the optimal method subsequently applied to evaluate wind resources in the South China Sea for 2023. The findings indicate that, while different interpolation methods minimally affect the correlation of wind speed data, there are notable differences in their impact on overall accuracy. The Cubic interpolation method proved to be the most effective, tripling spatial resolution and reducing wind speed errors in ERA5 data by 26%. Using this method, wind resource assessments were conducted in selected areas of the South China Sea. Results reveal that the annual available operational hours for wind turbines in most parts of the region range from 2000 to 4000 h, with fluctuations in turbine output power increasing alongside available operational hours.

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.

The deployment of energy storage systems can play a role in peak and frequency regulation, solve the issue of limited flexibility in cleaner power systems in China, and ensure the stability and safety of the power grid. This paper presents a comprehensive environmental impact analysis of a lithium iron phosphate (LFP) battery system for the storage and delivery of 1 kW-hour of electricity. Quantities of copper, graphite, aluminum, lithium iron phosphate, and electricity consumption are set as uncertainty and sensitivity parameters with a variation of [90%, 110%]. The results show that global warming potential is 9.08E+01 kg CO2 eq. and fossil resource use is 1.21E+03 MJ, with uncertainty ranges of [8.54E+01, 9.23E+01] and [1.15E+03, 1.23E+03], respectively. Electricity consumption during the manufacture and installation process is the greatest contributor to climate change (CO2 eq. emissions), accounting for 39.71% and largely owing to non-renewable sources, followed by cathode materials at 27.85% and anode materials at 18.36%. The disposal and recycling process offers emission reductions but requires an additional 1.17% use of fossil resources. Sobol T indices for the quantity of electricity are the highest for acidification, climate change, fossil resource use, and ionizing radiation. By considering the pathway of China’s electricity mix from 2020 to 2050 under the Paris Agreement’s 2° target, the potential for environmental emission reduction in the system is evaluated. The results show that the greener electricity mix could lead to a 24.59% reduction in acidification impact, a 35.74% reduction in climate change impact, a 33.24% reduction in fossil resource use, and a 44.13% reduction in ionizing radiation impact. This study offers a comprehensive view of the environmental impact reductions associated with the lithium iron phosphate battery and its industry.

With the increasing global demand for new energy sources, Photovoltaic (PV) is increasingly emphasized as a renewable energy source globally. Consequently, the assessment of PV resources has become crucial. Existing single frameworks and algorithms for PV resource assessment lead to low assessment accuracy. To alleviate the deficiency, this study proposes a two-layer network algorithm based on Xception and VGG19 for the evaluation of PV resources. The proposed method combines Xception convolutional neural network and VGG19 convolutional neural network. In addition, this study constructs a two-layer network framework based on the two-layer network algorithm. The feasibility and reliability of the proposed method are verified by simulating the proposed method under the case. Compared with existing algorithms, the proposed method and framework can improve the accuracy of PV resource assessment.

The purpose of this paper is to solve the problem of the waste water produced by pharmaceutical industry. However, the general biochemical treatment system is difficult to meet its processing requirements. As the water intake of regulating tank fluctuates greatly, the amount of coagulant is often added according to the experience in the coagulation pretreatment of pharmaceutical waste water, which makes the quality of coagulation effluent unstable. Based on mathematical model, the waste water treatment process of pharmaceutical factory is taken as an example, while the effect of coagulation as pretreatment process on pharmaceutical waste water treatment is analyzed. Meanwhile, a mathematical model is established to judge the dosage of coagulant to prevent water quality fluctuation of coagulation effluent. The experiment ensures that the effluent quality is stable within a range and reduces the impact on subsequent biochemical systems. The results show that the coagulation effect of waste water with high CODcr and high turbidity is greater than that of low load and low turbidity waste water. In addition, coagulation has a fairly good removal effect for turbidity. Based on the above findings, we concluded that the PAC dosage calculated by the model can stabilize the CODcr value of the condensate effluent in the 1600mg/L-1800mg/L range, and complete the stable mission of the effluent CODcr index.

As the global demand for renewable energy grows, solar energy is gaining attention as a clean, sustainable energy source. Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants. This study proposes an integrated deep learning-based photovoltaic resource assessment method. Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time. The proposed method combines the random forest, gated recurrent unit, and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment. The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape. The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes, indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm. [ABSTRACT FROM AUTHOR]