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Scientific quantitative description of air quality uncertainty remains a challenging task. The key to accurate and interpretable forecasting of air quality lies in combining spatial and temporal information and feature interpretability. This study proposes a novel spatio-temporal forecasting model based on deep learning with squeeze and excitation attention mechanism and interpretable feature selection strategy. Specifically, the data is collected from Beijing-Tianjin-Hebei region which is highly representative and convenient for accuracy comparison. And interpretable feature selection model effectively combines the muti-stage feature selection and Shapley additive explanation methods to identify individual features that contribute significantly to the forecasting target as base variable. Subsequently, a deterministic forecasting model based on improved deep learning with squeeze and excitation channel attention to adaptively adjust feature weights is constructed to implement air quality forecasting. Finally, feature interpretable model analyzes the changes in different air quality influencing factors as time advances, providing policy guidance for joint prevention and control of regional air pollution. Experimental and discussion results reveals that the proposed model can serve as an effective air pollution management and analysis tool.

Chloride-induced corrosion is commonly described as the “cancer” of concrete structures. Accurately calculating the distribution pattern of chloride ions within concrete at different service times is essential for conducting targeted durability design for concrete structures in chloride environments. Fick's diffusion equation makes a significant contribution to solving this problem. However, traditional analytical methods for solving diffusion differential equations are not applicable for solving the Fick diffusion equation considering the dual time-dependent effects due to the strong time-dependent effects of chloride diffusion coefficients (D) and surface chloride concentrations (Cs) in concrete. Additionally, the finite element method is very inefficient, and the accuracy of traditional machine learning methods is concerning. In this study, a feedforward neural network is constructed as a trial function and then incorporated into the Fick diffusion equation, considering the dual time-dependent effects, along with its initial and boundary conditions, to form residuals. Subsequently, a loss function is constructed based on residuals, leading to the formation of the Physical Information Neural Network (PINN) to obtain numerical solutions of the Fick equation considering the dual time-dependent effects of D and Cs. The effectiveness of the algorithm is validated through comparative analysis of the predictions from the PINN model and results obtained from finite element numerical simulations and field exposure experiments in the case studies. The results indicate that the PINN algorithm, after undergoing boundary condition concretization, maintains good predictive accuracy for the diffusion of chloride within concrete, even when considering the dual time-dependent effect of D and Cs.

Abstract Value at risk (VaR) and expected shortfall (ES) have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions, external regulations, and risk capital allocation. However, existing VaR estimation approaches fail to accurately reflect downside risks, and the ES estimation technique is quite limited owing to its challenging implementation. This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources. The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation. Specifically, this study proposes a VaR estimator by combining quantile regression with “Mogrifier” recurrent neural networks to capture the “long memory” and “clustering” properties of financial assets; while for estimating ES, this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios. In addition to the typical properties of financial assets, the model design is also consistent with heterogeneous market theory. An empirical application to four major global stock indices shows that our model is superior to other existing models.

Accurate prediction of shale gas production is important for the government to formulate energy policies. China’s shale gas production is characterized by limited data and unclear influencing factors. Grey prediction models hold advantages in solving problems with inadequate data and insufficient information. However, existing grey models for shale gas production prediction have drawbacks of insufficient characteristic expression and parameter optimization. Therefore, a new grey model with four parameters is proposed in this work. Firstly, the power index, accumulative order, and background value coefficient in the new model are optimized using the PSO algorithm. Then, based on an in-depth analysis of the model structure and tests using random samples, the new model’s compatibility and adaptability are demonstrated to be better than other similar grey models. Thirdly, the new model is applied to simulate China’s shale gas production. The results show that the comprehensive error of the new model is only 1.3626%, lower than that of the SGGM(1,1) and UGM(1,1) models, which are 1.6211% and 1.6575% respectively. Lastly, using the new model, China's shale gas production is predicted, which is expected to reach 752 hundred million cubic meters by 2030. After analyzing the rationality of the result, relevant suggestions are put forward.

This paper addresses the problem of cyber-attack detection in a discrete event system by proposing a novel model. The model utilizes graph convolutional networks to extract spatial features from event sequences. Subsequently, it employs gated recurrent units to re-extract spatio-temporal features from these spatial features. The obtained spatio-temporal features are then fed into an attention model. This approach enables the model to learn the importance of different event sequences, ensuring that it is sufficiently general for identifying cyber-attacks, obviating the need to specify attack types. Compared with traditional methods that rely on synchronous product computations to synthesize diagnosers, our deep learning-based model circumvents state explosion problems. Our method facilitates real-time and efficient cyber-attack detection, eliminating the necessity to specifically identify system states or distinguish attack types, thereby significantly simplifying the diagnostic process. Additionally, we set an adjustable probability threshold to determine whether an event sequence has been compromised, allowing for customization to meet diverse requirements. Experimental results demonstrate that the proposed method performs well in cyber-attack detection, achieving over 99.9% accuracy at a 1% threshold and a weighted F1-score of 0.8126, validating its superior performance.

The temperature is a critical factor that determines the unfrozen water content and ice content in the frozen soil. In view of mechanical properties of the frozen soil depend on the volume of components of the four-phase systems, thus the temperature has a significant impact on the mechanical behaviors and deformation properties. The thermal state of the embankment in permafrost regions has a significant seasonal difference, then the seismic performance of the embankment alters with the season. In addition, the seismic performance is highly influenced by the properties of the earthquake motion, especially the seismic intensity. Combining these factors, a numerical simulation was conducted in this study. In this study, taking a typical section of Qinghai-Tibet Railway as an example, a numerical case study on the seismic behavior of embankment was carried out using the dynamic explicit FEM code ABAQUS/Explicit. The El Centro excitation with different intensities was performed in numerical analysis and two distinct thermal states of the embankment in extreme cold and warm days were considered as well. The seismic behaviors of the embankment, including the acceleration responses, the strain response, and the displacement response, were studied effectively. This paper proposes approaches and methods to study the seismic failure mechanism of the infrastructures, and the results can serve as a scientific basis for resisting earthquakes and preventing disasters in cold regions.

BackgroundEhlers-Danlos syndrome (EDS) spinal deformity type 2 has clinical features similar to those of spondyloepimetaphyseal dysplasia with joint laxity, type 1 (SEMDJL1). They have similar clinical manifestations and a similar genetic basis, both of which can be caused by mutations in the B3GALT6 gene. Hence, genetic screening and careful clinical examination are key to the differential diagnosis of these two diseases.Case presentationA 4-month-old boy was admitted to our hospital in order to find the causes of developmental delay. The clinical examination revealed that the child was delayed, with an excessive range of motion of joints, patent foramen ovale, and was accompanied by skin aging; the child was suspected to have EDS. However, unlike EDS, the child had normal muscle tension, and at the same time had a spinal deformity, mild kyphosis, widened right hip joint space, as well as a special face, joint laxity, and slender fingers, which were typical characteristics of SEMDJL1. A gene analysis showed two suspicious mutations in the B3GALT6 gene: c.808G > A(p.(G270S)) and c.942G > C(p.(W314C)), which were verified to be compound heterozygous mutations by analyzing genes in his parents. This mutation was not included in the HGMD, ClinVar, and other mutation databases, and thus was a newly discovered mutation.ConclusionUsing the clinical and genetic analyses, this study reported a Chinese case with EDS-like SEMDJL1 for the first time. Two pathogenic mutations were discovered in the B3GALT6 gene: c.808G > A(p.(G270S)) and c.942G > C(p.(W314C)).

Abstract Background Tibetan pigs (TP) exhibit heritable adaptations to their hypoxic environments as a result of natural selection. However, candidate proteins that affect the sperm quality of boars on plateaus have not yet been clearly investigated. Methods In this study, to reveal the candidate proteins that affect the quality of spermatozoa of boars on plateaus, we analyzed the sperm quality using computer-assisted semen analysis (CASA) system and reactive oxygen species (ROS) levels. We also compared the proteomes of sperm proteomes between TP and Yorkshire pigs (YP) raised at high altitudes using the isobaric tags for relative and absolute quantitation (iTRAQ) in combination with the liquid chromatography-tandem mass spectrometry (LC–MS/MS) proteomic method, and confirmed the relative expression levels of the four proteins by western blotting. Results The sperm quality of the TP was superior to that of the YP on plateaus. Of the 1,555 quantified proteins, 318 differentially expressed proteins (DEPs) were identified. Gene ontology (GO) analysis revealed that the DEPs were predominantly associated with the sorbitol metabolic process, removal of superoxide radicals, cellular response to superoxide, response to superoxide and regulation of the mitotic spindle assembly. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched in pathways involved in the regulation of the actin cytoskeleton, glutathione metabolism, oxidative phosphorylation, and estrogen signaling. Based on the protein–protein interaction (PPI) network analysis, we identified 8 candidate proteins (FN1, EGF, HSP90B1, CFL1, GPX4, NDUFA6, VDAC2, and CP) that might play important roles and affect the sperm quality of boars on plateaus. Moreover, the relative expression levels of four proteins (CFL1, EGF, FN1, and GPX4) were confirmed by western blot analysis. Conclusions Our study revealed 8 candidate proteins (FN1, EGF, HSP90B1, CFL1, GPX4, NDUFA6, VDAC2, and CP) that affect the sperm quality of boar on plateaus and provide a reference for further studies on improving sperm quality and the molecular breeding of boars on plateaus.

DNA methylation plays an important role in reproduction. Tibetan pigs maintain high sperm motility, even under extreme conditions at high altitudes. However, the DNA methylation mechanisms underlying testicular plateau adaptations in Tibetan pigs remain unclear. We determined the genomic DNA methylation level in the testicular tissues of Tibetan pigs living at high altitudes (TP) and of Yorkshire pigs that migrated to high altitudes (YP) using the fluorescence method. Quantitative real-time PCR and western blots were used to measure the gene and protein expression levels, respectively, of three DNA methyltransferases (DNMT1, DNMT3A, and DNMT3B) and hypoxia-inducible factor 2α (HIF2α). We found that the genomic DNA methylation level and the mRNA and protein expression levels of the four genes were all significantly lower in the testicular tissues of TP than YP. In short, compared with YP, the downregulation of DNA methyltransferases in the TP testes led to lower levels of genomic DNA methylation, which would facilitate higher sperm motility. Meanwhile, HIF2α expression in the TP testes was down-regulated, which might contributes to TP testes’ adaption to the plateau environment. This study provided new insights into the DNA methylation mechanisms underlying plateau adaptations in TP testicles.

Wind speed forecasting is an essential procedure in electric grid dispatching. Short-term wind speed forecasting can be a great challenge and an intractable issue in increasing wind energy output and guaranteeing power safety. The current wind speed forecasting models are based on a single model, which is generally an artificial intelligence model or a statistical forecasting method. However, these models cannot perform well in all cases. An effective combined model is proposed in this paper, and this model includes four parts: weight coefficient optimization calculation based on the nonpositive constraint combination theory, singular-spectrum analysis, combined forecasting and discussion of results. The developed model can decrease the negative influences of the component models and maximize the advantages of each component model. To evaluate the forecasting accuracy of our proposed model, ten minutes of wind speed data from Shandong Peninsula, China, were used as test cases. It is clearly demonstrated that the developed combined strategy outperforms the individual forecasting methods in terms of forecast performance and stability.

Wind speed forecasting takes a significant place in electric system owing to the fact that it has significant influence on operation efficiency and economic benefits. Aimming at improving forecast performance, a substantial number of wind speed prediction models have been proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this study, a novel forecasting system is proposed consisting of three modules: data preprocessing module, individual forecasting module and weight optimization module, which effectively achieve better forecasting ability. For data preprocessing and individual forecasting module, more regular sequences are obtained by decomposition technology, and association features are extracted by deep learning algorithm in prediction module. In the weight optimized module, the combination method base on the multi-objective optimization algorithm and nonnegative constraint theory are used to improve the prediction effectiveness. The combination model successfully exceeds the limits of individual predicton models and comparatively improves prediction accuracy. The effectiveness of the developed combination system is evaluated by 10-min wind speed in Penglai, China. The experiment results indicate that proposed forecasting system is better than other traditional forecasting models on three real wind speed datasets indeed.

Power load forecasting has an influence of great signification on improving the operational efficiency and economic benefits of the power grid system. Aiming at improving forecast performance, a substantial number of load forecasting models are proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this article, a novelty hybrid model which combines data preprocessing technology, individual forecasting algorithm and weight determination theory is presented for obtaining higher accuracy and forecasting ability. In this model, an effective data preprocessing method named SSA is adopted to extract the load data characteristics and further improve the prediction performance. In addition, a combined forecasting mechanism composed of BP, SVM, GRNN and ARIMA is successfully established using the weight determination theory, which exceeds the limits of individual prediction models and comparatively improves prediction accuracy. And the thought of combine linear and nonlinear model together can further take the advantage of two kinds of models to forecast power load more effectively. To assess the validity of the combined model, four datasets of 30-minutes power load from Australia are selected for research. The experimental results show that the established model not only has obvious advantages over other individual models, but also can be applied as an available technology for electrical system programming.

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.

With the continuous expansion of the industrial production scale and the rapid promotion of urbanization, more and more serious air pollution threatens people’s lives and social development. To reduce the losses caused by polluted weather, it is popular to predict the concentration of pollutants timely and accurately, which is also a research hotspot and challenging issue in the field of systems engineering. However, most studies only pursue the improvement of prediction accuracy, ignoring the function of robustness. To make up for this defect, a novel air pollutant concentration prediction (APCP) system is proposed for environmental system management, which is constructed by four modules, including time series reconstruction, submodel simulation, weight search, and integration. It not only realizes the filtering and reconstruction of redundant series based on the decomposition-ensemble mode, but also the weight search mechanism is designed to trade off precision and stability. Taking the hourly concentration of PM2.5 in Guangzhou, Shanghai, and Chengdu, China as an example, the simulation results show that the APCP system has perfect prediction capacity and superior stability performance, which can be used as an effective tool to guide early warning decision-making in the management of environmental engineering.

In the development of the wind power industry, short-term wind speed forecasting is necessary, and many researchers have made substantial efforts to establish wind speed prediction models. However, realizing the accurate prediction of wind speeds remains a challenging task. The current prediction models do not consider the preprocessing of the data, and each model has various shortcomings. Considering the disadvantages of the available models, in this paper, an advanced combined forecasting system is applied that utilizes a data preprocessing strategy and parameter optimization strategy to obtain accurate prediction values. The proposed prediction system employs linear and nonlinear models that can take into account the characteristics of wind speed sequences, successfully combine the advantages of various single models, and yield accurate and stable prediction values. Finally, according to the experimental analysis and discussion, the proposed combined prediction system outperforms the compared models in prediction. In conclusion, the powerful combined prediction model provides a feasible scheme for wind power prediction.

Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting.

PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Following publication of the original article [1], the authors reported an error in Fig 5 of this article, graphs presenting FCM and immunofluorescent for CD4T, CD8T and NK cell of the Control Groups (LL2, LL2-irradation, MCS-irradiation) were inadvertently duplicated from another parallel experiment.

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO)-based Back Propagation Neural Network (BPNN) is proposed to establish wind speed interval forecasts (IFs) by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP) and IFs normalized average width (IFNAW). Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW) and validity (IFCP) of IFs is examined by coverage width-based criteria (CWC). As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

This article describes a model-free adaptive control method for knee joint exoskeleton, which avoids the complexity of human–exoskeleton modeling. An important feature of the proposed controller is that it uses the input and output data of the knee joint angle to control the exoskeleton. Furthermore, discrete sliding mode control law and prior torque are introduced to improve the accuracy and robustness of the system. Prior torque of knee joint is obtained through the walking simulation of human–exoskeleton modeling. Specially, the experiment is carried out by using the co-simulation automatic dynamic analysis of mechanical systems and MATLAB. Data from these assessments indicate that the proposed strategy enables the knee exoskeleton to track the trajectory of angle well and has a good performance on walking assistance.

Loess is widely distributed in the western part of China and its area comprises 6.6% of Chinese territory. Because of the characteristics of high porosity, low strength, and weak cementation, the loess is characterized by high seismic vulnerability which has been observed and confirmed by many researchers at home and abroad. The postquake field investigation and the laboratory study have shown that the ground motion effects, including the amplification effects and slope effects, were obvious in loess sites. Moreover, the causes of landslides, seismic subsidence, and liquefaction were mainly attributed to this special soil structure and properties. In this paper, based on the data of shear wave velocity of typical loess in Lanzhou region, combining the results of dynamic triaxial tests, the numerical analysis of ground motion effects in the loess regions is analyzed. The results reveal how the ground motion effects are checked and demonstrated the important role of numerical simulations while studying the characteristics of ground motions.

Abstract Background Although the whole tumor cell vaccine can provide the best source of immunizing antigens, there is still a limitation that most tumors are not naturally immunogenic. Tumor cells genetically modified to secrete immune activating cytokines have been proved to be more immunogenic. IL-18 could augment proliferation of T cells and cytotoxicity of NK cells. GM-CSF could stimulate dendritic cells, macrophages and enhance presentation of tumor antigens. In our study, we used mouse GM-CSF combined with IL-18 to modify Lewis lung cancer LL/2, then investigated whether vaccination could suppress tumor growth and promote survival. Methods The Lewis lung cancer LL/2 was transfected with co-expressing mouse GM-CSF and IL-18 plasmid by cationic liposome, then irradiated with a sublethal dose X ray (100 Gy) to prepare vaccines. Mice were subcutaneously immunized with this inactivated vaccine and then inoculated with autologous LL/2 to estimate the antitumor efficacy. Results The studies reported here showed that LL/2 tumor cell vaccine modified by a co-expressing mouse GM-CSF and IL-18 plasmid could significantly inhibit tumor growth and increased survival of the mice bearing LL/2 tumor whether prophylactic or adoptive immunotherapy in vivo. A significant reduction of proliferation and increase of apoptosis were also observed in the tumor treated with vaccine of co-expressing GM-CSF and IL-18. The potent antitumor effect correlated with higher secretion levels of pro-inflammatory cytokines such as IL-18, GM-CSF, interferon-γ in serum, the proliferation of CD4+ IFN-γ+, CD8+ IFN-γ+ T lymphocytes in spleen and the infiltration of CD4+, CD8+ T in tumor. Furthermore, the mechanism of tumor-specific immune response was further proved by 51Cr cytotoxicity assay in vitro and depletion of CD4, CD8, NK immune cell subsets in vivo. The results suggested that the antitumor mechanism was mainly depended on CD4+, CD8+ T lymphocytes. Conclusions These results provide a new insight into therapeutic mechanisms of IL-18 plus GM-CSF modified tumor cell vaccine and provide a potential clinical cancer immunotherapeutic agent for improved antitumor immunity.

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error).

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.

Two different pulse cleaning techniques for ultra-high contrast laser systems are comparably analysed in this work. The first pulse cleaning technique is based on noncollinear femtosecond optical-parametric amplification (NOPA) and second-harmonic generation (SHG) processes. The other is based on cross-polarized wave (XPW) generation. With a double chirped pulse amplifier (double-CPA) scheme, although temporal contrast enhancement in a high-intensity femtosecond Ti:sapphire chirped pulse amplification (CPA) laser system can be achieved based on both of the techniques, the two different pulse cleaning techniques still have their own advantages and are suitable for different contrast enhancement requirements of different laser systems.

As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried out in recent years, but unfortunately, these studies ignore the importance of data preprocessing and the influence of numerous missing values, leading to poor forecasting performance. In this paper, a hybrid forecasting system based on data preprocessing and an Extreme Learning Machine optimized by the cuckoo algorithm is proposed, which can overcome the limitations of the single ELM model. In the system, the standard genetic algorithm is added to reduce the dimensions of the input and utilize the time series model for error correction by focusing on the optimized extreme learning machine model. And according to screened results, the 5% fractile and 95% fractile are applied to compose the upper and lower bounds of the confidence interval, respectively. The assessment results indicate that the hybrid system successfully overcomes some limitations of the single Extreme Learning Machine model and traditional BP and Mycielski models and can be an effective tool compared to traditional forecasting models.

The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series’ forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR) model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.

In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Smart grids have been constructed so as to guarantee the security and stability of the power grid in recent years. Power transformers are a most vital component in the complicated smart grid network. Any transformer failure can cause damage of the whole power system, within which the failures caused by overloading cannot be ignored. This research gives a new insight into overload capability assessment of transformers. The hot-spot temperature of the winding is the most critical factor in measuring the overload capacity of power transformers. Thus, the hot-spot temperature is calculated to obtain the duration running time of the power transformers under overloading conditions. Then the overloading probability is fitted with the mature and widely accepted Weibull probability density function. To guarantee the accuracy of this fitting, a new objective function is proposed to obtain the desired parameters in the Weibull distributions. In addition, ten different mutation scenarios are adopted in the differential evolutionary algorithm to optimize the parameter in the Weibull distribution. The final comprehensive overload capability of the power transformer is assessed by the duration running time as well as the overloading probability. Compared with the previous studies that take no account of the overloading probability, the assessment results obtained in this research are much more reliable.