In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the ...environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural ...wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China’s Changma wind farm and Spain’s Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Wind energy is important to the transformation and development of global energy, because it is clean and renewable. However, the productivity of wind power is low due to its volatility, randomness, ...and uncertainty. Therefore, a new hybrid prediction model based on combined Elman-radial basis function (RBF) and Lorenz disturbance is proposed, which can promote the productivity of wind power by better predicting wind speed, firstly, applying the variational mode decomposition (VMD) algorithm to original nonstationary wind speed data to obtain several relatively stationary intrinsic mode functions (IMF), so as to fully exploit its potential characteristics. Meanwhile, the sample entropy is introduced to determine the decomposition number
K
. Afterwards, different IMF components with different characteristics are used for training and prediction: Elman neural network with sensitivity to historical state data is used for wind speed trend components; RBF with strong nonlinear mapping capability is adopted for other stochastic modal components. Next, the first-step prediction values can be obtained by reconstructing the predicted results of the respective IMF components. Finally, the Lorenz equation is introduced in view of the effects of atmospheric disturbances on wind fluctuations, which can be used to revise the first-step prediction results to obtain more realistic prediction results. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) VMD can solve the problem of modal aliasing in empirical mode decomposition, to obtain a better decomposition result; (2) the combined prediction method of Elman and RBF is used for modal components, that is, different algorithms are adopted for different components, which have better prediction effects; (3) in this research, the results of the proposed combination prediction model is more accurate by comparison with the other neural network models. This research work will help the power system to rationally formulate wind farm control strategies, enhance the self-regulation of wind farm, and further promote global energy innovation.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Hierarchical nitrogen-doped porous carbons (HNPCs) with tunable pore structures and ultrahigh specific surface areas were designed and prepared from sustainable biomass precursor cellulose carbamate ...via simultaneous carbonization and activation by a facile one-pot approach. The as-synthesized HNPCs exhibited an ultrahigh specific surface area (3700 m2 g-1), a high pore volume (3.60 cm3 g-1) and a high level of nitrogen-doping (7.7%). The HNPCs were structurally tunable in terms of their pore structure and morphology by adjusting the calcination temperature. In three-electrode systems, the electrode made of HNPCs prepared at 900 degree C (HNPCs-900) showed a high specific capacitance of 339 F g-1 in 6 M KOH aqueous electrolyte and 282 F g-1 in 1 M H2SO4 electrolyte at a current density of 0.5 A g-1. An outstanding rate capability ( similar to 73% retention at a current density of 20 A g-1) and excellent cycling stability ( similar to 95% retention after 5000 galvanostatic charge-discharge cycles at a current density of 5 A g-1) in KOH electrolyte were achieved. In two-electrode systems, the electrode made of HNPCs-900 exhibited a high specific capacitance of 289 F g-1 at 0.5 A g-1 and good rate capacity ( similar to 72% retention at a current density of 20 A g-1) as well as cycling stability ( similar to 92% retention at 2 A g-1) after 5000 cycles. Furthermore, the HNPCs-900 showed an unprecedented adsorption capacity for methylene blue (1551 mg g-1) which was among the few highest ever reported for dye removal. The HNPCs could be used as functional materials for energy storage and waste water treatment.
Accurate wind power prediction provides significant guarantee for power grid dispatching, and wind speed prediction, as the basic link of wind power forecasting, has crucial theoretical research ...significance and practical application value. In this paper, we present the wind speed prediction of IPSO-BP neural network based on Lorenz disturbance. At first, the data is processed by principal component analysis (PCA) to select the key factors affecting wind speed, which can effectively reduce the complexity of model. Then, the improved particle swarm optimization (IPSO) algorithm is used to globally optimize the weights and thresholds of BP neural network, and overcome the problem of local minimum value. The initial prediction results can be obtained by the IPSO-BPNN model. Finally, Lorenz system is introduced to correct the initial prediction value and improve forecasting accuracy. According to the wind farm data of Spain and Chang Ma in China, we take an empirical research to analyze the optimization effect of IPSO algorithm and the promotion effect of Lorenz system on the precision of preliminary forecasting. The results are as follows: 1) IPSO algorithm accelerates the convergence rate of weights and thresholds of BP neural network and 2) Lorenz disturbance system obviously weakens the random volatility of wind speed, effectively modifies its preliminary prediction results, and upgrades its prediction accuracy.
Tremendous energy consumption appears as rapid economic development, leading to large amount of CO
2
emissions. Although plentiful studies have been made into the driving factors of CO
2
emissions, ...the existing literatures that take the spatial differences and temporal changes into consideration are few. Therefore, this study first analyzes the variations of total CO
2
emissions’ spatial distribution from 2008 to 2017 and present the changes of driving factors, finding significant spatial autocorrelation in CO
2
emissions at the province level, and that urbanization rate, per capita GDP and per capita CO
2
emissions increased significantly, but energy consumption structure and trade openness decreased. We then compared the effects of different factors affecting CO
2
emissions, using classic linear regression model, panel data model, and the geographically weighted regression (GWR) model, and the three models roughly agree on the effects of factors. The GWR model considering spatial heterogeneity provides more detailed results. Population, urbanization rate, per capita carbon emissions, energy consumption structure, and trade openness all have positive effects, while per capita GDP has a two-way impact on CO
2
emissions. The influence of urbanization rate and energy consumption structure in the central and western regions increased even faster than in eastern regions, and the impacts of trade openness in lower and higher opening areas are more significant. The population and per capita CO
2
emission have declining influences, among which the influence of population in coastal areas declined more slowly, while the rate of decline of per capita CO
2
emission was positively correlated with the local total CO
2
emissions. The Lorenz curve and the Gini coefficient reveal the inequality distribution of CO
2
emissions in various regions, with the highest CO
2
emissions growth in the medium-economic-level areas, where the key area of carbon mitigation is. Finally, per capita GDP reveals that China as a whole has the trend of inverted N-shape Kuznets curve, and the underdeveloped regions are in the rising stage between the two inflection points, while developed regions are at the end of the rising stage and about to reach the second inflection point.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Considering the strong fluctuation and the nonlinearity of wind speed, and atmospheric uncertainties, wind speed prediction based on the hybrid model is presented, which is composed of the neural ...network, variational mode decomposition (VMD), and Lorenz disturbance. First, the VMD is used to process the data to get several intrinsic mode functions (IMFs). Second, the neural network model (NN model) can be established by these IMFs of the training set, and the validation set is used to adjust the model parameters. Subsequently, given the nonlinearity of wind speed, Lorenz disturbance is added to determine the finial model, and the best Lorenz disturbance parameter and the best Lorenz disturbance sequence can be obtained by minimizing the mean absolute error of validation set. At last, the wind speed can be forecasted by the hybrid model. Taking Sotavento wind farm in Spain as an example, the results show that, the hybrid model has stable prediction performance, and the distribution characteristics of its results are consistent with the actual wind speed. The general model only focuses on improving prediction accuracy. However, on the basis of improving the forecasting accuracy, the proposed model not only enhances the prediction stability, but also restores the characteristics of wind speed. This research work provides a more scientific basis for wind power dispatching arrangement, and it is of great significance to improve the utilization rate of wind power.
Polyphenol, characterized by various phenolic rings in the chemical structure and an abundance in nature, can be extracted from vegetables, grains, chocolates, fruits, tea, legumes, and seeds, among ...other sources. Tannic acid (TA), a classical polyphenol with a specific chemical structure, has been widely used in biomedicine because of its outstanding biocompatibility and antibacterial and antioxidant properties. TA has tunable interactions with various materials that are widely distributed in the body, such as proteins, polysaccharides, and glycoproteins, through multimodes including hydrogen bonding, hydrophobic interactions, and charge interactions, assisting TA as important building blocks in the supramolecular self-assembled materials. This review summarizes the recent immense progress in supramolecular self-assembled materials using TA as building blocks to generate different materials such as hydrogels, nanoparticles/microparticles, hollow capsules, and coating films, with enormous potential medical applications including drug delivery, tumor diagnosis and treatment, bone tissue engineering, biofunctional membrane material, and the treatment of certain diseases. Furthermore, we discuss the challenges and developmental prospects of supramolecular self-assembly nanomaterials based on TA.
Biomass-derived O- and N-doped porous carbon has become the most competitive supercapacitor electrode material because of its renewability and sustainability. We herein presented a facile approach to ...prepare O/N-doped porous carbon with cotton as the starting material. Absorbent cotton immersed in diammonium hydrogen phosphate (DAP) was activated at 800 °C (CDAP800s) and then was oxidized in a temperature range of 300-400 °C. The electrochemical capacitance of the impregnated cotton was significantly improved by doping with O and N, and the yield was improved from 13% to 38%. The sample oxidation at 350 °C (CDAP800-350) demonstrated superior electrical properties. CDAP800-350 showed the highest BET surface area (1022 m
2
g
−1
) and a relatively high pore volume (0.53 cm
3
g
−1
). In a three-electrode system, the CDAP800-350 electrodes had high specific capacitances of 292 F g
−1
in 6 M KOH electrolyte at a current density of 0.5 A g
−1
. In the two-electrode system, CDAP800-350 electrode displayed a specific capacitance of 270 F g
−1
at 0.5 A g
−1
and 212 F g
−1
at 10 A in KOH electrolyte. In addition, the CDAP800-350-based symmetric supercapacitor achieved a high stability with 87% of capacitance retained after 5000 cycles at 5 A g
−1
, as well as a high volumetric energy density (18 W h kg
−1
at 250 W kg
−1
).
Biomass-derived O- and N-doped porous carbon has become one of the most competitive supercapacitor electrode material because of its renewability and sustainability.
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The stability of gossypol was investigated by the spectroscopic method. Gossypol was dissolved in three different solvents (CHCl₃, DMSO, and CH₃OH) under different storage conditions (dark and with ...nitrogen protection, natural light and with nitrogen protection, ambient air conditions) for different time intervals (0 days, 3 days, 5 days, 7 days, 15 days, 30 days, and 45 days) at room temperature. Then, the stability of gossypol was investigated by ¹H NMR, UV-vis, and HPLC-QTOF-MS spectrometry. Results showed that gossypol existed in aldehyde-aldehyde form in chloroform within five days. Then, both aldehyde-aldehyde and lactol-lactol tautomeric forms existed and maintained a stable solution for 45 days. Gossypol dissolved in methanol mainly existed in aldehyde-aldehyde form. Only a tiny amount of lactol-lactol was found in freshly prepared methanol solution. Gossypol was found to only exist in lactol-lactol form between 30-45 days. Gossypol existed in aldehyde-aldehyde, lactol-lactol, and ketol-ketol forms in dimethyl sulfoxide, and there was a competitive relationship between aldehyde-aldehyde and lactol-lactol form during the 45 days. Among all the solvents and conditions studied, gossypol was found to be highly stable in chloroform. Under the tested conditions, the natural light and atmospheric oxygen had little effect on its stability. Although the spectroscopy data seemed to be changed over time in the three different solvents, it was actually due to the tautomeric transformation rather than molecular decomposition.
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