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This study proposes a novel hybrid prediction model for short-term Chl-a concentration prediction in the Chaohu Lake basin. The model adopts a two-layer decomposition, grouping prediction, and summation design, with an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) used for data decomposition and sample entropy (SampEn) for determining the complexity of the decomposed components. The component with the highest SampEn is then further decomposed using variational mode decomposition (VMD). In the grouping prediction part, a hybrid prediction model is utilized, based on bidirectional gate recurrent unit (BiGRU), temporal convolutional network (TCN), and attention mechanism (AM), to predict all components after the second decomposition separately. The final Chl-a concentration prediction is obtained by linearly summing the predicted values of each component. Through comparison with sensitivity analysis, point estimation, and interval estimation, this study finds that the proposed ICEEMDAN-VMD-BiGRU-TCN-AM model performs well in predicting the content of Chl-a concentration in lakeshore. The model has the highest fitting determination coefficient of 98.79%, the smallest root-mean-square error (RMSE) of only 0.0063 (ug/L), and the largest probability interval coverage percentage (PICP) of 97.57%. These results indicate that the IVBTA model has significant advantages in accuracy, precision, and stability compared to other models. This means that our method can more accurately predict the content of Chl-a in reservoirs, providing a new approach for water quality prediction and effective prevention and control of eutrophication in reservoirs. [ABSTRACT FROM AUTHOR]

Swift and precise classification of rock masses significantly enhances the prudent and efficient advancement of rock mass engineering. To expeditiously and accurately determine the engineering classification of slope rock masses, this paper introduces a hybrid Data Comprehensive Weight Analysis (DCWA)-Equilibrium Optimizer (EO)-AdaBoost ensemble prediction model. This model is formulated to prognosticate revised Basic Quality value ([BQ]) and the engineering classification of slope rock masses, utilizing the Basic Quality (BQ) classification method. Six elements have been chosen to formulate the prognostic index, and a dataset comprising information on the slope rock mass of 266 groups has been assembled for the purpose of training and evaluating the predictive accuracy of the established model. Two slope sections in DEZIWA open pit mine is chosen to validate the correctness of established DCWA-EO-AdaBoost model in the field through numerical simulation. In comparison to AdaBoost, DCWA-AdaBoost, DCWA-EO-AdaBoost, K-Nearest Neighbor (KNN), Back Propagation (BP), Decision Tree (DT), and Random Forest (RF) models, it is discerned that the DCWA-EO-AdaBoost hybrid model exhibits elevated coefficient of determination (R2, 0.986), variance accounted for (VA, 98.64%), prediction accuracy (A, 92.31%), and kappa coefficient (KC, 88.87%). Conversely, the mean absolute error (E, 5.97%) and root mean square error (ER, 38.37) are diminished, affirming its reliability and superiority. Field validation reveals that, combine the DCWA-EO-AdaBoost model and BQ classification method, mechanical parameters used for slope stability analysis can be obtained accurately, and can obtains slope stability analysis results basically consistent with the engineering practice through numerical simulation, which can prove the correctness of the established prediction model and clarify the use of the established model in the process of slope stability analysis. This attests that the established predictive model holds substantial merit, offering a noteworthy reference for the expeditious and precise acquisition of [BQ] and the classification of slope rock mass based on the BQ method, and can be adopted in the process of slope stability analysis. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

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Next activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively. [ABSTRACT FROM AUTHOR]

CO2 emissions have contributed to global warming and belong to high-noise, non-stationary and nonlinear systems. An accurate prediction method for annual CO2 emissions can improve the effectiveness of emission reduction policies. However, the existing prediction methods for small-scaled samples (i.e., hourly or daily time series) are unsuitable for regional policy benchmarks. Hence, a novel hybrid prediction model under data decomposition mode is developed for annual CO2 emissions in this work. For illustration, the five representative CO2 emissions (i.e., China, United States, India, Russian, and Japan) from 1970 to 2019 are collected to verify performance, which are taken from Global Carbon Project. The results show that the average prediction accuracy of the proposed prediction model is up to 97.95%, which whole performance improved by more than 1.61% compared with others. The total of five countries' annual CO2 emissions in 2020 (18,311.72 metric tons) is approximately equal to that in 2018 (18,353.63 metric tons). The proposed model is a reliable prediction tool for annual CO2 emissions and can assist policymakers in adjusting reduction measures and regulators to access the current effects. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals.

In this paper, we propose a modified hybrid prediction model to capture both linear and nonlinear patterns in time‐series data by incorporating autoregressive integrated moving average (ARIMA) models and Taylor expansions. We introduce a time‐varying gain in the tracking differentiator to reduce the peaking value that occurs in a constant high‐gain design. The models are tested with gold and silver futures prices. The results show that the hybrid model with time‐varying high gain tracking differentiator outperforms other hybrid models. [ABSTRACT FROM AUTHOR]

Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems. [ABSTRACT FROM AUTHOR]

Complex curved thin-walled structures, typically produced by multi-axis milling, are highly susceptible to deformation induced by residual stress. It is, therefore, that there is a considerable amount of research on developing predictive models for machining-induced residual stress. However, these developed models for residual stress prediction mainly focus on turning and three-axis milling. In the current study, a hybrid model combining experimental results and a finite element (FE) model is established to predict the residual stress profile of Ti-6Al-4 V titanium alloy for multi-axis milling. The residual stress profile is fitted by using the hyperbolic tangent function with the firefly algorithm (FA) based on the simulation and experiment. The R2 values change from 85.3 to 99.1% in the σx direction and change from 80.7 to 98.1% in the σy direction, which indicates a high fitting accuracy. The radial basis function (RBF) neural network is employed to establish the association between the process parameters and the model coefficients. Thus, the residual stress profile can be expressed by the cutting speed, feed rate, and inclination angle. And the prediction accuracy is verified to achieve 92.7% and 91.4% in the σx and σy directions, respectively. The effects of the cutting speed, feed rate, and inclination angle on surface residual stress and response depth are investigated. The findings demonstrate a strong nonlinear connection between process parameters and surface residual stress. The proposed hybrid prediction model of residual stress can be used for further machining optimization of complex curved thin-walled structures. [ABSTRACT FROM AUTHOR]

Abstract Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R 2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R 2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R 2 = 0.9931) and (RMSE = 0.6019 mD, R 2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available.

Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R2 = 0.9931) and (RMSE = 0.6019 mD, R2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available. [ABSTRACT FROM AUTHOR]

An accurate prediction of a driver’s head pose in the in-vehicle information system (IVIS) tasks is essential for both distraction warning and vehicle takeover rule-making in human–machine codriving. In this study, a real-world experiment is conducted to collect data on driver behavior and vehicle state during the IVIS tasks under different road conditions. An innovative scheme is proposed for multistep time-series prediction of a driver’s head pose. The proposed scheme consists of two main parts: a data-processing module and a model module. Furthermore, a combination of a machine-learning-based method and a geometric method is used to estimate a driver’s head pose, and the MediaPipe model is used to extract the hand data. In the model module, a convolutional neural network (CNN), which can automatically capture short-term local dependencies in the head pose data, and a bidirectional long short-term memory (BiLSTM), which can capture long-term macro dependencies, are combined. In addition, an attention mechanism is used to weigh important time periods and variables. Finally, the performance of the proposed hybrid CNN-BiLSTM-attention model in the head pose prediction is validated, and the proposed model is compared with three traditional machine-learning-based models and the CNN-BiLSTM model. The comparison results show that the proposed model outperforms the other models in the prediction steps and can provide reliable driver’s head pose data for distraction warning and takeover rules. [ABSTRACT FROM AUTHOR]

Simple Summary: The hybrid model is proposed to predict humidity in sheep barns, based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model, and required hyperparameters in SVR were optimized, adopting the CGWO algorithm to avoid the local extremum problem. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics; it achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083, in terms of MAE, RMSE, MSE, and NRMSE, respectively, and a maximum value of 0.9973, in terms of the R2 index. Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R2 index. [ABSTRACT FROM AUTHOR]

A model to predict photosynthetic carbon assimilation rate (A) with high accuracy is important for forecasting crop yield and productivity. Long short-term memory (LSTM), a neural network suitable for time-series data, enables prediction with high accuracy but requires mesophyll variables. In addition, for practical use, it is desirable to have a technique that can predict A from easily available information. In this study, we propose a BLSTM- augmented LSTM (BALSTM) model, which utilizes bi-directional LSTM (BLSTM) to indirectly reproduce the mesophyll variables required for LSTM. The most significant feature of the proposed model is that its hybrid architecture uses only three relatively easy-to-collect external environmental variables—photosynthetic photon flux density (Q in), ambient CO2 concentration (C a), and temperature (T air)—to generate mesophyll CO2 concentration (C i) and stomatal conductance to water vapor (g sw) as intermediate outputs. Then, A is predicted by applying the obtained intermediate outputs to the learning model. Accordingly, in this study, 1) BALSTM (Q in, C a, T air) had a significantly higher A prediction accuracy than LSTM (Q in, Ca, Tair) in case of using only Q in, C a, and Tair; 2) BALSTMCi,gsw, which had C i and g sw as intermediate products, had the highest A prediction accuracy compared with other candidates; and 3) for samples where LSTM (Q in, Ca, Tair) had poor prediction accuracy, BALSTMCi,gsw (Q in, Ca, Tair) clearly improved the results. However, it was found that incorrect predictions may be formed when certain factors are not reflected in the data (e.g., timing, cultivar, and growth stage) or when the training data distribution that accounts for these factors differs from the predicted data distribution. Therefore, a robust model should be constructed in the future to improve the prediction accuracy of A by conducting gas- exchange measurements (including a wide range of external environmental values) and by increasing the number of training data samples. [ABSTRACT FROM AUTHOR]

In order to realize the prediction of the remaining useful life (RUL) of the low-voltage conventional circuit breaker mechanical system in the whole life cycle, a prediction method based on opening-related vibration signal and stage attention hybrid neural network is proposed. The opening vibration signal contains richer mechanical state information. Therefore, the variational mode decomposition (VMD) based on energy loss is used to reconstruct the signal to increase the time-domain identification of the shock characteristics of the vibration signal, and the double threshold method (DTM) based on short-time energy entropy ratio (STEER) is used to realize the segmentation of the opening-related vibration signal. A stage attention mechanism (SA) is proposed, combining the convolutional neural network (CNN) and the gated recurrent unit (GRU) to construct the SA-CNN-GRU hybrid model. The first-stage distributed attention (DA) acts on the CNN to refine the mining of the information sensitive to degradation in the time and feature dimensions, and the second-stage time-step attention (TA) acts on the GRU to learn the time-series gradual information of the degradation process in a targeted manner and eliminate the influence of the degradation uncertainty of the complex system. Using the vibration data of three circuit breakers to test, the results show that the proposed method can solve the RUL prediction problem of circuit breaker mechanical systems and have high prediction stability, and the introduction of SA can effectively improve the prediction performance of the model. [ABSTRACT FROM AUTHOR]

Blast furnace (BF) permeability index is a crucial parameter that can quickly, intuitively, and comprehensively reflect the furnace condition. Accurate prediction of this index is crucial for optimising production efficiency and ensuring the stable operation of the BF. In this study, a hybrid permeability index prediction model is constructed by combining a least squares support vector machine and an artificial neural network, using the mean shift clustering algorithm (MSCA) to classify the BF conditions is applied. The results show that the MSCA algorithm shows remarkable precision in classifying the stable and unstable operating states of BF, achieving an impressive accuracy rate of 93.98%. The hybrid prediction model could accurately predict the permeability index and has a mean absolute error of 0.6877, a mean square error of 0.4721 and an R2 of 0.9215, highlighting its robust predictive performance. These findings underscore the practical significance of our model in enhancing BF operational efficiency and reliability. [ABSTRACT FROM AUTHOR]

A novel intelligent hybrid model is proposed in this paper for accurate prediction and modeling of solar power plants using the wavelet transform package (WTP) and generative adversarial networks (GANs). In the proposed model, the wavelet transform is deployed for decomposing the solar energy signal into the sub-harmonics followed by the statistical feature selection analyses. The GAN model as a deep learning approach is proposed for learning each sub-frequency and predicting the future of the solar energy in the short-time window. Due to the high complexity of the solar irradiance data when training, an evolutionary algorithm based on dragonfly algorithm (DA) is suggested to train the generative and discriminator networks in the GAN. Moreover, a three-phase adaptive modification is suggested to enhance the search capabilities of the DA optimization. The efficiency and appropriate performance of the proposed model is examined and compared with the most successful models such as artificial neural networks (ANNs), support vector machine (SVM), time series, auto-regressive moving average (ARMA), and original GAN on several benchmarks for varied forecast time horizons. The simulation results on the datasets of two regions show the mean absolute percentage error (MAPE) of 0.0282 and 0.0262, when 1-pace forecast horizon, for the two regions which increase up to 0.0531 and 0.0631 for 6-pace forecast horizon. Moreover, the root mean absolute error (RMSE) is 0.0473 and 0.0479 for the two regions at 1-pace forecast horizon which increased up to 0.0895 and 0.0946 for 6-pace forecast horizon. These results show the more precise performance of the proposed forecast deep learning as well as the more optimal performance of the modified DA over the other algorithms shown in the results.

Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems.

• A novel hybrid system is proposed with deterministic prediction and interval prediction. • Outlier detection and correction method are used to reduce the complexity of the series. • Data decomposition strategy is applied to decompose the original series to improve the prediction accuracy. • A multi-objective optimization algorithm is selected to determine the original weights and biases of the forecasting model. • Applied interval prediction to quantify the range of concentration change of air pollutants caused by uncertainties. A novel system regarding deterministic and interval predictions of pollutant concentration is constructed in this study, which can not only obtain higher prediction accuracy in deterministic prediction and also provide effective interval prediction of air pollutant concentration. In the deterministic prediction stage, the improved extreme learning machine combines outlier detection and correction algorithm, data decomposition strategy, and a multi-objective optimization algorithm to form a hybrid model for predicting pollutant concentration. Moreover, the applicability of the optimization algorithm was verified from theoretical and experimental analysis. In the interval prediction stage, three distributions are compared to mine, the traits of deterministic prediction errors are analyzed, and interval prediction is designed to quantify the uncertainties associated with pollutant concentration. To investigate the prediction performance of the proposed system, comparison experiments have been executed using the PM 2.5 concentration series from three cities. The results indicate that the system proposed in this paper outperforms comparison models in forecasting accuracy and has advantages for pollution prediction. [ABSTRACT FROM AUTHOR]

Major nations across the globe are increasingly concerned about the rising trends in carbon dioxide (CO 2) emissions, particularly in societies of varying scales. Against this backdrop, precise prediction of carbon emissions becomes critically important, especially for the formulation and adjustment of near-term carbon reduction policies. However, the non-linear, non-stationary, and complex nature of daily carbon emission data poses a great challenge for daily-level forecasting especially in the big data context. To address this issue, we propose a novel composite forecasting approach named DCEF dedicated to the estimation of daily carbon emissions. In concrete, our approach employs the Empirical Mode Decomposition (EMD) for data stabilization and the Auto-regressive Integrated Moving Average (ARIMA) model for forecasting, while integrating the Truncated singular value decomposition(TSVD) technique for data compression and mitigating noise. Finally, DCEF is empirically validated with real daily carbon emission datasets collected from 6 sectors in 13 countries of varying sizes. Experimental results demonstrate the advantages of our approach compared to other baseline models in terms of prediction accuracy and efficiency. • Establish a model that is capable of daily CO 2 emissions forecasting in 6 sectors. • Improves prediction accuracy and efficiency with the proposed ensemble model. • Model performance consistently outshines competitors in 13 country datasets. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective.

Currently, how to effectively control carbon emissions is a global concern. Accurate carbon price prediction can help us more effectively control carbon emissions and reduce environmental pollution. Due to the complexity of carbon valence, this study proposes an improved decomposition ensemble prediction model. Firstly, a novel signal decomposition algorithm is designed to adaptively determine the number of decomposition modes for carbon price sequences, obtaining a set of intrinsic mode functions (IMFs). Secondly, a creative method for assessing the contribution of feature components is developed. It utilizes a genetic algorithm-based mutual-information method to calculate the contribution of each IMFs to the original sequence, classifying all feature components into high and low contribution components. Then, this study developed a differential learning method that partitions the contribution of feature components and applies targeted learning and integration using temporal convolutional network with different structures. Compared to traditional forecasting frameworks, this framework improves prediction performance while reducing structural complexity and computational costs. Empirical results demonstrate the superiority and robustness of the proposed model with the outcomes of this study present the performances that the proposed model outperforms the others with Guangzhou's mean absolute percentage error of 3.30313%, which are beneficial for governments and enterprises to predict adverse signals in the carbon market in a more timely manner, and to take measures in advance to maintain the stability of carbon emissions. [ABSTRACT FROM AUTHOR]

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft's four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace. [ABSTRACT FROM AUTHOR]

Wind energy resources are a clean renewable energy source. Accurate prediction of wind speed has important theoretical significance and practical value for sustainable use of wind energy, energy planning, and economic development. This paper proposes a hybrid model for prediction of wind speed using support vector machine and genetic programming models, with error compensation for the prediction residuals. Wind speed and the residuals generated using the hybrid model are simulated and predicted. The proposed model is applied to monthly average wind speed data observed over the period January 2005–December 2014 from the Tuoli and Hetian stations in Xinjiang, China. For each time series, eight prediction schemes, including independent support vector machine model and genetic programming model and their combination, were tried. At the same time, phase space reconstruction method was used to select model input factors. The results clearly show the performance differences of the eight wind speed prediction schemes in the prediction effect of the two time series. The combination of the support vector machine wind speed prediction model and the genetic programming residual prediction model has the best prediction effect, with a correlation coefficient value above 0.9, mean square value below 0.1, and mean absolute error value below 0.2. This combined model is found to be quite efficient and, therefore, the proposed approach is highly suitable to predict the monthly average wind speed. [ABSTRACT FROM AUTHOR]

Through analysis of the carbon emissions transfer network formed by the exchange of intermediate products among industries, we can promote the realization of national carbon emissions reduction goals. Therefore, it is of great significance to build a prediction model of the carbon emissions transfer network for more accurate predictions. According to the characteristics of the random oscillation sequence (ROS) of interindustry carbon emissions transfer, a hybrid prediction model denoted as the ROGM-AFSA-GVM is proposed based on the grey model (GM) for ROS and the general vector machine (GVM) optimized by the artificial fish swarm algorithm (AFSA). The proposed model uses the ROGM model to predict the general ROS trend and relies on the AFSA-GVM model to predict the nonlinear law of ROS. The predicted values of the two parts are combined to obtain predicted interindustry carbon emissions transfer values. The proposed model is used to simulate the interindustry carbon emissions transfer network of China. The simulation results show that the ROGM-AFSA-GVM model can effectively resolve the prediction problem of ROS. Comparing the predicted networks with the actually measured networks, it is verified that the proposed model is suitable for simulating the interindustry carbon emissions transfer network and has a good prediction performance.

The strip floating height is a key factor affecting production efficiency and quality of the air cushion furnace. At present, the air cushion furnace with hybrid nozzles is the most typical heating and drying equipment. In order to predict the floating height of the strip in this equipment, a hard-division and multi-model based floating height prediction method is proposed. In hard division method, the process is divided into several stable and vibration stages. On the one hand, time interval based novel density clustering algorithm is proposed so as to avoid the wrong division caused by noise at hard division stage. On the other hand, covariance matrices based local density is presented to better capture dynamic characteristics of the process. In the multi-model stage, a hybrid prediction model under stable state and XGBoost-based model under vibration state are established to predict the strip floating height. The hybrid prediction model is composed of mechanism model and data driven model. The mechanism model is proposed by combining the force balance equation and inviscid theory which can predict the major information of the floating height and the data driven model compensates the error of the mechanism model. A mount of experiments have been carried out on the existing air cushion experimental platform and show desirable prediction effect.

Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM–CGWO–SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM–CGWO–SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R2 index.

Accurate short-term wind power prediction is crucial for the efficient and safe operation of wind power systems. To enhance the accuracy of short-term wind power prediction, this paper proposes a hybrid short-term wind power prediction model, IDBO-VMD-TCN-GRU-Attention, based on the Improved Dung Beetle Optimizer algorithm (IDBO), Variational Modal Decomposition (VMD), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism. The IDBO is used to optimize the parameters of the VMD. Then the optimized IDBO-VMD is used to decompose the original data into modal components with lower volatility to fully extract the data features. These modal components are inputted into the TCN-GRU-Attention to predict the short-term wind power and obtain the final prediction value. The model is tested and validated using real data from four different months and compared against 11 other models. Results demonstrate that our proposed model significantly reduces the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by an average of at least 22.86 %, 18.82 %, and 19.99 %, respectively, across the four different months. This indicates that the proposed model provides a superior fit to the data, offers higher prediction accuracy, and holds significant practical value. [ABSTRACT FROM AUTHOR]

• Constructing a fast prediction model of urban temperature based on WRF and TFT. • Retaining the atmospheric dynamic characteristics in urban temperature prediction. • WRF-TFT model effectively predicting urban temperature with mean error of 0.8°C. • Fast prediction model providing guidance for urban risk management and regulation. Urban Heat Island (UHI) poses a significant challenge to the sustainable development of global cities. It is of great importance to efficiently characterize the spatiotemporal distribution of urban temperatures for UHI mitigation strategies, such as urban ecosystem planning and control. Numerical Weather Prediction (NWP) methods are used to obtain the urban temperature distribution. However, NWP requires significant hardware resources and long computation time. The development of artificial intelligence approaches have been applied in expediting the weather forecasting, yet their forecasting precision remains significantly inferior to that of NWP. Hence, this study aims to propose a hybrid fast prediction model, considering the accuracy of WRF (Weather Research and Forecasting) and efficiency of Temporal Fusion Transformer (TFT) neural networks. By integrating high-precision temperature time series boundaries generated by WRF into TFT, this method (WRF-TFT) is able to realize the rapid predictions of urban temperature distributions (around 15 times faster compare to WRF) while maintaining the physical characteristics of atmospheric dynamics. With this method, we also conducted for future temperature forecast for cities. It is estimated that the temperature can exceed 35 °C more than 12 hours per day in July 2050. This hybrid model facilitates swift acquisition of urban temperature trends, providing a crucial basis for urban risk management and planning. [ABSTRACT FROM AUTHOR]

In the prediction stage of finite control set-predictive torque control for permanent magnet synchronous machine (PMSM), the mismatched parameters will inevitably bring about the predictive errors, which will cause the wrong selection of voltage vector in the optimization stage, and even affect the stability of the entire control system. In order to eliminate the prediction errors, a discrete hybrid prediction model-based predictive torque control (DHPM-PTC) of PMSM is proposed to improve robustness. In the stator current prediction model, the traditional open-loop prediction model is abandoned, and the closed-loop model prediction equation is established. In the stator flux prediction model, this article gets rid of the traditional open-loop integral prediction model, and establishes the closed-loop flux prediction model integrating the current model and the voltage model. Secondly, the stability and parameter design principles of the closed-loop current prediction model and the closed-loop flux prediction model are discussed. Finally, the verification of the proposed algorithm is made on a PMSM test platform. The test results indicate that the proposed DHPM-PTC is superior to the traditional predictive torque control (T-PTC) in dynamic, steady and robust performances. [ABSTRACT FROM AUTHOR]

It is important to study building energy consumption considering the current state of global climate and its close relationship with building energy consumption. This study proposes a hybrid Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) model to predict energy consumption using a hospital building located in Beijing as a case study. First, a simulation is performed using building simulation software and the model is calibrated using energy bill and electricity meter data. Subsequently, the energy consumption of the calibrated model is decomposed to determine the energy consumption of the chillers and pumps. Finally, a BiLSTM model is established and the WOA is employed to enhance the generalization and performance of the BiLSTM model. Results show 1) for hour scale, the Mean Absolute Percentage Error (MAPE) of the WOA-BiLSTM model is reduced by 2.93 % and 0.75 % over the WOA-LSTM and BiLSTM models, respectively. 2) the time-lag effect of energy consumption is discussed and the results show that the optimal window size is approximately 18 h. 3) the optimization results of different iterations and population size of WOA show that performance is optimal when 400 iterations and a population size of 5, and larger datasets require more iterations based on a comparison of two datasets (hour and day scales). [ABSTRACT FROM AUTHOR]

To mitigate the threat to power system caused by ramp events – large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. This approach contains two successive stages of work, including wind power forecasting and ramp detection. Considering high‐performance ramp prediction requires long‐term and accurate wind power prediction results; this study also proposes a hybrid prediction model at the first stage. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long‐term trend prediction. Then, a multivariate model is built with a data‐mining algorithm to correct system errors of the primary prediction, which is addressed to improve long‐term prediction performance. At the second stage, a modified swinging door algorithm is applied for ramp detection. Performance of both the proposed long‐term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach. [ABSTRACT FROM AUTHOR]

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• This paper forecasts the wave height using a novel hybrid model VMD-RCMFE-E-GRU. • A wave height data preprocessing algorithm based on VMD-RCMFE is proposed. • E-GRU enhances the overall prediction performance. • VMD-RCMFE-E-GRU outperformed other models for all three buoy station datasets. • The VMD-based model was found to be superior in each step-ahead prediction. As waves are exploited as renewable energy, developing new forecasting technologies is essential to accurately predict significant wave height. In this study, a wave height prediction model is proposed that includes a decomposition–reconstruction framework and ensemble gated recurrent unit (E-GRU). First, variational mode decomposition (VMD) decomposes the original wave height series into multiple subsequences to decrease the uncertainty and nonstationarity of the data. Second, the refined composite multiscale fuzzy entropy (RCMFE) is adopted to reconstruct subsequences into several components to reduce the computational complexity. A GRU is then used to predict each component individually to capture more temporal information from historical data. Finally, we predict all the subresults by E-GRU and obtain the final result. We test the proposed hybrid model using wave height data from three buoy stations. The comparative evaluation of prediction accuracy across four models is achieved through the utilization of key performance metrics, namely, the coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E NS), Legates and McCabe index (E LM), Willmott's index (WI), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Among these metrics, the VMD-RCMFE-E-GRU model distinctly emerges as the superior performer for forecasting hourly wave height across all stations and future temporal intervals, as evidenced by its notably lowest RMSE, MAE, and MAPE values, coupled with the highest R2, E NS , E LM , and WI scores. The case results show that the proposed hybrid model is more effective for wave height prediction. [ABSTRACT FROM AUTHOR]

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

Short-term prediction of tanker freight rates (TFRs) is strategically important to stakeholders in the oil shipping industry. This study develops a hybrid TFR prediction model based on an artificial neural network (ANN) and an adaptive genetic algorithm (AGA). The AGA adaptively searches satisficing network parameters such as input delay size. The ANN iteratively optimizes a prediction network considering parsimonious variables and time-lag effects as predictors. Three parsimonious variables (crude oil price, fleet productivity and bunker price) are selected by a stepwise regression of TFR variables. The article compares the performance of its hybrid model with two traditional approaches (regression and moving average), as well as with the findings of existing ANN studies. The results of our model (root mean squared error (RMSE)=11.2 WS) are not only significantly superior to the regression approach (RMSE=21.6 WS) and the moving average approach (RMSE=17.5 WS), but are even slightly superior to the results of existing ANN studies (RMSE=14.6 WS-15.8 WS). [ABSTRACT FROM AUTHOR]

Nowadays, the missing data imputation is the novel paradigm to replace with the imputed value of the missing attribute. The missing data occurs due to bias information, non-response of the system. In the medical domain, it becomes the major challenge to impute the both categorical and numerical data. In this paper, the Grey Fuzzy Neural Network is proposed for missing data imputation in the mixed database. Initially, the WLI fuzzy clustering mechanism is utilized to generate the different clusters in which the medical data are grouped together. Then, we intend to integrate the Grey Wolf Optimizer (GWO) with the ANFIS network model, termed the Grey Fuzzy Neural Network (GFNN). The proposed method is mainly used to determine the optimal parameters to design the membership function. Finally, the hybrid prediction model is used to find out the imputed data for both categorical and numerical. In the hybrid prediction model, the categorical data is then imputed by the distance measure. The experimental results are validated, and performance is analysed by metrics such as MSE and RMSE using MATLAB implementation. The outcome of the proposed GFNN attains lower 0.13 MSE, and 0.35 RMSE ensures to impute the data significantly in the missing attribute of the mixed database. [ABSTRACT FROM AUTHOR]

Wind speed interval prediction is essential for wind power production. The possible variation interval of wind speed cannot be reflected in deterministic prediction, which is an excellent opportunity to build up interval prediction. This paper proposed a hybrid interval prediction model based on temporal inception convolutional network (TICN) and variational mode decomposition (VMD). Firstly, Pearson correlation is used to determine the decompose level of variational mode decomposition, and the simplified sub-series can be used to extend the trend of wind speed. Then referring to the concept of inception network, the proposed interval prediction model is built up with multiple temporal convolutional networks with different kernels. At last, data sets from real wind farms are used to evaluate the performance, and experiment results show the proposed model outperforms all other benchmark models in multi-step interval predictions. It is indicated that the proposed wind speed interval prediction model has better performance and can be used in practice. • Applying Pearson correlation to determine the decomposition level of VMD. • Temporal convolution neural network speeds up the training while keeping accuracy. • Forming the network into inception block enhanced the ability of generalization. • Comparison result with 7 other benchmark models proves the effectiveness. [ABSTRACT FROM AUTHOR]

Considering solar photovoltaic (PV) usage in building energy systems (BES), energy systems that combine batteries and solar PV (PVB) have been widely used in buildings. Ensuring the safety of the battery and the operation requirements of other types of equipment to the extent possible while achieving the optimization goal has become an important challenge in the implementation of this system. In this case, model predictive control (MPC) has gained increasing attention in the operation control of BES owing to its strong fitting ability. However, the prediction models at the core of MPC are not adequately designed for the BES optimization problem, and numerous studies have not yet been modeled and tested in real buildings. In this study, we used a long- and short-term hybrid prediction model in the MPC framework. The proposed prediction models and MPC framework were validated using a measured dataset from a real office building in Japan. The results show that, compared with the currently used baseline control logic, the proposed hybrid prediction MPC framework can improve the battery safety index by 81.6%, while the combined heat and power continuous operation index can also be improved by 36.4%. Compared with the conventional MPC framework, the proposed hybrid prediction MPC framework improves the optimization of off-grid operation by approximately 69% while maintaining the remaining optimization objectives. • Off-grid oriented operation and equipment safety are considered at the same time. • Hybrid prediction model is designed for model predictive control. • Modeling and algorithm are based on the measured data of real existing buildings. [ABSTRACT FROM AUTHOR]