Search

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China's shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard's block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise's block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers. [ABSTRACT FROM AUTHOR]

With the continuous expansion of urban scale in China, the increase of passenger flow has brought great pressure to the urban public transport system. An accurate and timely prediction of the short-term passenger flow at each metro station is extremely important for the metro intelligent control system to make a timely decision. In this paper, based on the measured passenger flow data of Zijingshan station of Zhengzhou Metro Line 1, an improved whale optimization algorithm is proposed to predict the passenger flow on different time scales. The results show that the method has higher accuracy than the traditional least squares support vector machines algorithm. The paper opens a new window for nowcasting warning in the rush hour and long-period optimization of the public transport. [ABSTRACT FROM AUTHOR]

The water yield of aquifers increases the risk of water inflow, threatens the safe production of coal mines, and even causes geological disasters and construction hazards. To predict water yield quickly and accurately, multiple composite factors are used to invert unit water inflow rates to judge water yield grade. Taking the typical representative of north China-type coal fields as an example, six factors are selected: aquifer thickness, the radius of influence, normalized drawdown, permeability coefficient, the core rate of drilling holes, and the proportion of clay thickness to the thickness of the lower group. The whale optimization algorithm (WOA)–convolutional neural network (CNN)–support vector machine (SVM) model is established with the unit water inflow rate as the forecast target, and different models are selected for comparison. The water yield zoning map is obtained by bringing the borehole data into the model for prediction. The findings indicate that the root mean square error and average absolute error of the composite predictive model models are 0.0318 and 0.0268, respectively, and the model outperforms alternative models. The predicted water yield zoning aligns well with the actual conditions, offering a novel paradigm for water yield assessment. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is crucial for China's power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China's novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated. [ABSTRACT FROM AUTHOR]

Group train operation (GTO) applications have reduced the tracking intervals for overloaded trains, and can affect the efficiency of rail transport. In this paper, we first analyze the differences between GTO and traditional operation (TO). A new mathematical model and simulated annealing algorithm are then used to study the problem of mixed group train operation. The optimization objective of this model is to maximize the transportation volume of special heavy-haul railway lines within the optimization period. The main constraint conditions are extracted from the maintenance time, the minimum ratio of freight volume, and the committed arrival time at each station. A simulated annealing algorithm is constructed to generate the mixed GTO plan. Through numerical experiments conducted on actual heavy-haul railway structures, we validate the effectiveness of the proposed model and meta-heuristic algorithm. The results of the first contrastive experiment show that the freight volume for group trains is 37.5% higher than that of traditional trains, and the second experiment shows a 30.6% reduction in the time during which the line is occupied by trains in GTO. These findings provide compelling evidence that GTO can effectively enhance the capacity and reduce the transportation time cost of special heavy-haul railway lines. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale optimization algorithm, and RVM tuned with Manta Ray foraging optimization concerning root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R²) and Nash-Sutcliffe Efficiency (NSE) and new graphical inspection methods. The models were assessed using data acquired from two stations in China and data were divided into three equal parts. The models were tested using each data set. The application outcomes revealed that the proposed algorithm considerably improved the accuracy of a single RVM in monthly pan evaporation prediction by an average improvement in RMSE, MAE, R², and NSE as 27.65%, 27.53%, 8.40% and 8.63%, respectively. It is also found that the proposed algorithm showed significant dominance over others models with respect to improvement in overall mean values of RMSE, MAE, R², and NSE statistics from 34.7-38.2 to 18.2-19.5, 36.2-36.4 to 19.1-18.5, 12.5-13.8 to 3.6-3.7, and 12.4-14.6 to 3.6-3.9%, for both climatic stations, respectively. Importing extraterrestrial radiation and periodicity component (month number of the data) into the model inputs improved the prediction accuracy of the implemented models. The outcomes revealed that the RVM-IMRFO performed superior to the other methods in predicting monthly pan evaporation using only temperature data which is essential, especially in developing countries where other climatic data are missing or unavailable. The RVM model was also compared with standard multi-layer perceptron neural networks (MLPNN) and found that the first acts better than the latter in monthly pan evaporation prediction. [ABSTRACT FROM AUTHOR]

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model. [ABSTRACT FROM AUTHOR]

One of the biggest difficulties, which China's power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data's short-term predictions. At the same time, it also reduces the time of personnel debugging. [ABSTRACT FROM AUTHOR]

Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results. [ABSTRACT FROM AUTHOR]

China's leisure diving tourism has made gratifying achievements in the past years. It is necessary to evaluate the leisure diving tourism's development level, and then the disadvantages of the leisure diving tourism can be found and the effective measures can be taken to develop the leisure diving tourism in China. A fuzzy comprehensive evaluation model based on the bat algorithm is proposed to solve the shortcomings of the fuzzy comprehensive evaluation method, which lacks a theoretical basis and completely depends on experience. Firstly, the basic conception of leisure diving tourism is discussed. Also, the main affecting factors of leisure diving tourism are analyzed. The evaluation model of leisure diving tourism developing level is constructed based on the fuzzy comprehensive method. The weight of the fuzzy comprehensive evaluation method is optimized by the bat algorithm to realize the optimal selection of weight. The fuzzy evaluation matrix is used to construct the judgment matrix to determine the weight of each evaluation index, and the bat algorithm is used to adaptively select the weight of the judgment matrix. Finally, three diving clubs are used as research objects to carry out evaluation analysis The results show that the fuzzy comprehensive evaluation method can effectively obtain the developing level of leisure diving tourism. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [ABSTRACT FROM AUTHOR]

Energy emission systems are often influenced by external information and policy shock. However, conventional grey models ignore the existence of the impact of multiple shock events and cumulative time-delay effects, which are crucial for achieving accurate forecasts. From a dynamic perspective, we aim to establish a multivariable grey prediction model considering the impact of multiple shock events, namely MSGTDM(1,N) model. Specifically, to accurately describe the impact of different types of external shocks on the system, three kinds of nonlinear dynamic shock functions are designed, including growth dynamic shock function, decline dynamic shock function, and slope dynamic shock function. Based on this, multi-dimensional instantaneous shock utility terms and cumulative time-delay utility terms are constructed to accurately measure shock effects. And Whale Optimization Algorithm is employed to determine the optimal parameters of the shock functions through comprehensive comparative analysis. The findings affirm the MSGTDM(1,N) model's higher predictive accuracy and validity. Consequently, the forecast results of China's carbon emissions from coal and natural gas consumption in 2022–2025 provide a reliable basis for adjusting the energy structure and implementing the "dual-carbon" policy effectively. [ABSTRACT FROM AUTHOR]

Wind energy exhibits strong fluctuations and intermittencies. The accurate prediction of wind speed is of considerable significance for the operation and maintenance of wind farms and the safety of the power grid. However, previous studies have often ignored the impact of data noise on trend prediction, and lacked effective data pre-processing methods and adaptive interval prediction schemes, resulting in poor prediction results. To improve the accuracy of prediction, this paper proposes a wind-speed combination interval prediction system based on a fuzzy strategy and neural network. This study used a fuzzy strategy to pre-process the data and proposed a joint optimization algorithm that uses multiple multi-objective metaheuristic algorithms to optimize the neural network jointly. An improved prediction hybrid algorithm was used to reconstruct the predicted results. Finally, based on the fuzzy theory and neural network, interval prediction schemes that can adapt to different situations were proposed. Wind-speed data from the Penglai Wind Farm in China were used for verification. The mean absolute percentage error of the wind-speed point prediction was 3.75%, and the prediction interval coverage probability of the wind-speed interval prediction was 97.92%. The experimental results showed that the proposed model not only outperforms other comparable models but also improves the prediction interval coverage probability by 176.4% compared with the baseline model. This proves that the integrated hybrid model can improve the accuracy and effectiveness of wind-speed prediction. The accurate interval prediction of wind speed can result in the complementary scheduling of renewable energy generation and the sustainable development of the energy field, and support intelligent upgrades. • Wind speed data is reconstructed using a fuzzy information granulation method. • Integration of multiple meta-heuristic algorithms for optimizing neural networks. • Integration of neural networks after multiple optimized structures. • An optimized backpropagation neural network has great potential for prediction. [ABSTRACT FROM AUTHOR]

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results. [ABSTRACT FROM AUTHOR]

Comprehensive learning particle swarm optimization (CLPSO) is a powerful metaheuristic for global optimization. This paper studies parallelizing CLPSO by open computing language (OpenCL) on the integrated Intel HD Graphics 520 (IHDG520) graphical processing unit (GPU) with a low clock rate. We implement a coarse-grained all-GPU model that maps each particle to a separate work item. Two enhancement strategies, namely, generating and transferring random numbers from the central processor to the GPU as well as reducing the number of instructions in the kernel, are proposed to shorten the model's execution time. This paper further investigates parallelizing deterministic optimization for implicit stochastic optimization of China's Xiaowan Reservoir. The deterministic optimization is performed on an ensemble of 62 years' historical inflow records with monthly time steps, is solved by CLPSO, and is parallelized by a coarse-grained multipopulation model extended from the all-GPU model. The multipopulation model involves a large number of work items. Because of the capacity limit for a buffer transferring data from the central processor to the GPU and the size of the global memory region, the random number generation strategy is modified by generating a small number of random numbers that can be flexibly exploited by the large number of work items. Experiments conducted on various benchmark functions and the case study demonstrate that our proposed all-GPU and multipopulation parallelization models are appropriate; and the multipopulation model achieves the consumption of significantly less execution time than the corresponding sequential model. [ABSTRACT FROM AUTHOR]

In response to poor economic efficiency caused by the single service mode of energy storage stations, a double-level dynamic game optimization method for shared energy storage systems in multiple application scenarios considering economic efficiency is proposed in this paper. By analyzing the needs of multiple stakeholders involved in grid auxiliary services, fully tap into the profitability potential of energy storage stations. The capacity of the shared energy storage system is optimized by the non-dominant sorting beluga whale optimization algorithm (NSBWOA) in the upper level, and the operation strategy under multiple scenarios is optimized by the adaptive greedy search algorithm (AGSA) in the lower level. With the goal of maximizing the gross annual total income and high-value peak regulation ratio, and minimizing the cost- income ratio, the optimal capacity configuration and operation strategy of the shared energy storage system are obtained through collaborative optimization between upper and lower level models. The effectiveness of the proposed method is verified through the simulation testing of actual operating data of a certain power grid in China. Simulation results show that the gross annual income and high-value peak regulation ratio across multiple scenarios (Scenario III) are the highest, and the cost-income ratio is at an acceptable low level, which can provide a theoretical basis for the large-scale application of energy storage systems in new power systems. • The profit relationship between multiple stakeholders in auxiliary services and energy storage needs is explored. • Double-level optimization control model for shared energy storage system in multiple application scenarios is established. • The combinatorial optimal scheduling problem in decision sets is solved by adaptive greedy search algorithm. • The non dominated sorting beluga whale optimization algorithm effectively solves multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

A problem-specific routing algorithm integrating ant colony optimization (ACO) and integer-coded genetic algorithm (GA) is developed to address the newly observed limitations imposed by ultranarrow aisles and access restriction, which exist in the largest e-commerce enterprise with self-run logistics in China. Those limitations prohibit pickers from walking through the whole aisle, and the access restriction even allows them to access the pick aisles only from specific entrances. The ant colony optimization is mainly responsible for generating the initial chromosomes for the genetic algorithm, which then searches the near-optimal solutions of picker-routing with our novel chromosome design by recording the detailed information of access modes and subaisles. To demonstrate the merits of the proposed algorithm, a comprehensive simulation for comparison is conducted with 12 warehouse layouts with real order information. The simulation results show that the proposed hybrid algorithm is superior to dedicated heuristics in terms of solution quality. The impacts of the parameters with respect to warehouse layout on the picking efficiency are analyzed as well. Setting more connect aisles and cross aisles is suggested to effectively optimize the picking-service efficiency in the presence of access limitations. [ABSTRACT FROM AUTHOR]

An accurate prediction of lake water levels is of great significance to water resource regulation, flood prevention and mitigation. However, water level fluctuations have been increasingly serious due to abnormal climate and extreme events. In view of this, a VMD-EF-OBILSTM model was constructed for lake water levels based on multiple sources of hydrological and meteorological variables. In this model, water level data are transformed into low-frequency internal and high-frequency external terms by variable modal decomposition (VMD), and they are combined with external factors (EF) for multivariate prediction. The optimized bi-directional long short-term memory (OBILSTM) invokes the attention mechanism and optimizes the model's hyperparameters by whale optimization algorithm (WOA). Ultimately, the predictions of each component are linearly combined to obtain the forecast values. The empirical results with water level data from Poyang Lake in China show that the multi-source deep learning model can achieve higher prediction accuracy and lower prediction uncertainty. • A deep learning model combined VMD-EF-OBILSTM for lake levels prediction is proposed. • Key parameters of the VMD can be adjusted automatically and optimally using the IWOA. • The adaptive function of IWOA based on E-EK can evaluate IMF features in several ways. • IMF is divided into internal and external terms for self and multivariate prediction. • The VMD-EF-OBILSTM includes external factors and performs better than standard models. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Accurate prediction of global solar radiation (R s) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting R s in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in R s estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting R s in various regions of China. •For standalone models, the SVM had higher prediction accuracy than DT and BP. •Three hybrid models (ACO-SVM, CS-SVM and GWO-SVM) were proposed to predict daily R s in different climatic zones of China. •The accuracy of hybrid models was higher than the standalone SVM model. •The GWO-SVM model performed better than the CS-SVM model, and the accuracy of both were better than ACO-SVM model. [ABSTRACT FROM AUTHOR]

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the "optimal" parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping. [ABSTRACT FROM AUTHOR]