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Energy system autonomous control is influenced by day-ahead forecasting, despite being carried out independently in the literature. This paper develops an energy management modular platform that integrates multi-variable timeseries prediction and autonomous energy infrastructure scheduling in real-time. Firstly, an ensemble prediction model is developed for the day-ahead multi-energy and renewable power prediction, which is implemented by coupling variants of CNN, GRU, and BiLSTM models into a global model using an ensemble approach. Secondly, a deep reinforcement learning (DRL) with a soft actor critic (SAC) algorithm that include safety-guided network to make the policy network constraint-aware is proposed. A multi-energy system with renewable energy and carbon capture technology is then anticipated as the energy infrastructure for achieving a carbon neutral community and evaluating our proposed model. The proposed models are trained and tested on a real-world dataset from Arizona, USA. The ensemble prediction model achieved the least root mean squared error (RMSE). On the other hand, the improved DRL method exhibits superior performance in reducing energy cost, minimum constraint violation, and fast deployment compared to state-of-the-art DRL methods. Finally, the two models are coupled and carry out generalization performance on the prediction and energy management scheme, including the sensitivity analysis on carbon capture price, in the case studies. • A novel ensemble prediction model is proposed for multi-variable prediction. • A novel safe soft-actor DRL is proposed for real-time autonomous energy management. • The proposed method deals with physical constraint and security issue. • The coupling prediction and safe DRL control achieved optimal result. [ABSTRACT FROM AUTHOR]

Coal-fired boilers considering deep peak shaving operate under wide and variable load requirements with complex and varying working conditions, making the prediction and optimization of combustion target variables challenging. This study proposed a data-driven approach for the prediction model building, tuning and combustion optimization. In this approach, a neural network for prediction based on Gaussian kernel and long short-term memory (LSTM), which captured and fused the working condition and temporal information, was first proposed. To address incomplete historical data coverage of working conditions, a fine-tuning framework with the idea of transfer and few-shot tuning was then introduced. Finally, an improved fitness function considering both optimization objectives and combustion adjustment limitations was designed to mitigate the risks associated with large-scale adjustments of operable variables. By conducting experiments on a 1000 MW boiler, targeting NOx concentration and the outlet temperature of the air pre-heater, the proposed method demonstrated high prediction accuracy with R 2 values of 0.979 and 0.989, and significantly reduced mean squared error (MSE) by 98.05% and 97.37% on new conditions compared to untuned models. It achieved the reductions in NOx and temperature of 24.70 mg m − 3 and 2.013 ∘ C , and decreased the average changes of operable variables by 9.66% and 11.24% compared to using only rigid constraints. [Display omitted] • Proposed a Gaussian kernel and LSTM structure to capture and fuse information. • Introduced the idea of transfer and few-shot tuning for combustion prediction models. • Validated the fine-tuning framework for online combustion model tuning. • Provided soft constraints on variable adjustments using an improved fitness function. [ABSTRACT FROM AUTHOR]

A novel pseudo-spectral method is proposed to tackle the non-linear control problem of a wave energy converter (WEC) integrated with an all-electrical power-take-off (PTO). The WEC hydrodynamic model is constructed using linear wave theory together with a non-linear viscous damping term. The PTO model encompasses a permanent magnet synchronous generator (PMSG) whose local field weakening mechanism imposes non-linear operational constraints on the WEC's motion and PTO torque. The energy-maximizing control problem of this wave-to-wire model is non-linear and has not been investigated before. In comparison to pseudo-spectral methods previously studied, the proposed pseudo-spectral method is exempt from the assumption of ideal incoming wave prediction. This is made possible by the introduction of a novel mapping function to convert the finite control horizon onto the Gaussian quadrature interval. This mapping function increases more slowly with time at the beginning of the control horizon where wave prediction attains greater precision. As a result, the collocation points are utilized to the advantage of the better approximation of the objective function, thus better overall control performance. • A novel model predictive control (MPC) for wave energy converters (WEC) is proposed. • The case study involves a WEC integrated with an electric power take-off model. • Non-linear dynamics and constraints can be handled explicitly and optimally. • The novel MPC controller is robust against. wave prediction errors. [ABSTRACT FROM AUTHOR]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [ABSTRACT FROM AUTHOR]

Electricity is one of the most important energy sources for the progress of human society. Photovoltaic power generation (PPG) has great potential as a renewable energy source to support sustainable development. However, the unpredictability of photovoltaics because of the uncertain weather conditions, time lags and the complex interactions among related factors may result in instabilities in PPG, which limits its applications in the electric power system. Therefore, accurately identifying the related factors and representing the interactions are the key issues in predicting PPG. For this purpose, a novel seasonal grey prediction model with time-lag and interactive effects, denoted as SGMLI(1, n), is developed to predict the capacity of photovoltaic power generation. More specifically, a time-lag driving term, a periodic driving term, and an interaction driving term are proposed to represent the lag relationship, periodicity, and the interactive effects between two factors, respectively. Utilizing the least square method and the Whale Optimization Algorithm (WOA), the optimal solution to the nonlinear programming problem is estimated. For elaboration and verification, six grey forecasting models, two statistical techniques, and two machine learning models, are employed for comparison with the SGMLI(1, n) model. Experimental results reveal that the SGMLI(1,n) outperforms benchmark models in forecasting PPG. • A seasonal grey model with with time-lag and interactive effects is developed. • The new model can deal with the prediction under the interactions of related factors. • The novel model adapts to the time series characterized by time delay and periodicity. • The new method improves the applicability of the grey forecasting model. • Empirical results demonstrate the novel model's excellent forecasting performance. [ABSTRACT FROM AUTHOR]

The determination of the pressure-bearing performance of hydrogen storage tanks (HST) is integral to enhancing their operational safety and risk management capabilities. In this study, an inaugural development of low-cost critical failure pressure (BP c) prediction model for small samples was proposed, which was based on a combination of the fuzzy grey relational analysis (FGRA) and GM(1,N). Specifically, a total of 15 of BP c -related factors were proposed for analysis and projections based on the data from bonfire tests. Results from the FGRA indicated burst pressure was most closely linked to the initial filling pressure (R ij1 = 0.939) of tanks under fire scenario. The normal working pressure (R ij2 = 0.924) and the elasticity modulus (R ij3 = 0.871) also demonstrating significant impacts. Furthermore, three GM(1,N) prediction models which could estimate BP c with multi-factor coupling were developed. Increasing the number of input feature factors could enhance the predictive capability of GM model. The GM(1,16) had optimal prediction performance among the models, achieving a prediction accuracy of 99.8 %. As well, the mean absolute error (MAE) was 0.799 MPa while the mean absolute percentage error (MAPE) was 1.56 %. This paper offered a novel option for establishing the HST critical failure criterion safely and efficiently. • Obtained factors on design features, physicochemical, and external thermal load. • Clarified the main controlling factors which affected critical failure pressure. • Developed GM(1,N)-based models to predict critical failure pressure. • Suggestions on the safety of hydrogen storage tanks in service were proposed. [ABSTRACT FROM AUTHOR]

Battery health prognostics management is an important prerequisite for ensuring its safe use. As the battery is charging and discharging, its capacity deteriorates. Incremental capacity (IC) analysis is a common tool to analyse the degradation process based on the change rate of capacity relative to voltage. However, measuring battery voltage and current is difficult and there are inherent errors. This paper improves the IC curve from the perspective of time series, extracts a health indicator (HI), and uses the hybrid prediction model to predict the remaining useful life (RUL). Firstly, we convert the traditional IC-voltage (IC-V) curve to the IC-time (IC-T) curve. The time series corresponding to the peak of the improved curve is extracted. Secondly, the extracted degradation HI is predicted using grey forecasting model, and a probabilistic and iterative hybrid battery prediction method is established combined with Gaussian process regression. Finally, public datasets are used to validate the proposed method at different starting points and comparisons with other prediction methods are carried out. Results show that the HI based on the improved IC curve is highly correlated with the degradation process and the proposed method can provide an accurate result, which verifies its effectiveness and rationality. • This paper extracts the time corresponding to the improved IC-T curve peak as a new HI. • An iterative structure and probabilistic prediction method for RUL is established. • Grey forecasting model and Gaussian process regression are combined for RUL prediction. • Validation of the proposed method at different starting points for RUL is performed. [ABSTRACT FROM AUTHOR]

In Cameroon, predicting CO 2 emissions from road transports is crucial since it contributes in formulating the government's energy plans and strategies. In this perspective, this paper proposes an optimized wavelet transform hausdorff multivariate grey model (OWTHGM(1,N)) that addresses some of the weaknesses of the classical GM(1,N) model such as inaccurate prediction and poor stability. In all, three improvements have been made to the classical GM(1,N): First, all inputs are filtered using the wavelet transform, thereby denoising variables that could hamper modelling; second, a new time response function is established using the Hausdorff derivative; and finally, the use of Rao's algorithm to optimise the model's parameters as well as the ξ -order accumulated value of the observation data described by the Hausdorff derivative. In order to demonstrate the effectiveness of OWTHGM(1,N), it is applied to predict CO 2 emissions from road transport. The new model produces predictions with 1.27 % MAPE and 79.983 RMSE, and is therefore more accurate than competing models. OWTHGM(1,N) could therefore serve a reliable forecasting tool and used to monitor the evolution of CO 2 emissions in Cameroon. The forecast results also serve as a sound foundation for the formulation of energy consumption strategies and environmental policies. • The OWTHGM(1,N) model enhances CO 2 predictions in Cameroon's road transport sector. • The WT filters inputs, improving accuracy by denoising variables in the model. • Hausdorff derivative captures non-linear trends in emissions data. • Rao's algorithm optimizes model parameters for reliable forecasting. • OWTHGM(1,N) provides reliable forecasts with a MAPE of 1.27 % and RMSE of 79.983 [ABSTRACT FROM AUTHOR]

Construction prediction is the key for the shape control of energy storage salt caverns, which benefits with the storage capacity and long-term operational safety. However, the conventional grid discretization methods using elastic grid could not accurately tracking the three-dimensional boundary movements of salt cavern. This paper introduced a novel construction prediction model of salt cavern using a Volume-of-fluid based method. The tracking of the salt caverns' dissolving boundary is successfully reconstructed and tracked by the fluid volume fractions (0-1) in structural grids. The model was validated by indoor experiment and field data. In the simulation of indoor experiment, the lateral expansion, which was difficult to reproduce in previous models, is successfully simulated. The volume of the simulation chamber is 392.8 ml with an error of 1.9 %. During the simulation of Volgograd horizontal cavern construction, the simulated cavern shape is close to the sonar detection, with an average error in radius about 3.6 %. And the brine-discharge concentration is consistent with the site monitoring, with an average error about 4.5 %. These results validate the capability of the proposed method in three-dimensional dynamic boundary tracking and shape prediction of salt cavern. • A salt cavern construction prediction model is introduced using a Volume-of-Fluid method. • Three dimentional simulation of the movement of dissolving salt cavern interfaces is achieved for the first time. • A demo C++ program has been developed for numerical implementation of the proposed model. • model's reliability is verified through simulations of an indoor experiment and a field project. [ABSTRACT FROM AUTHOR]

Uncrewed underwater vehicles (UUVs) significantly benefit from phase change material (PCM)-based ocean thermal energy harvesting for long mission duration. However, this technology relies on different parameters that are critical to its efficiency. Sea trials indicated that a design using this technology has lower performance than its initial specifications. This underperformance results from different factors—mainly, the UUV's trajectory, travel time, temperature fluctuations, and biofouling on the heat exchanger due to long-term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance for mission planning. Two major parameters that influence the energy harvesting system include the pressure inside the hydraulic accumulator and the electrical load. This work focuses on the hydraulic-to-electric energy conversion system. A combination of numerical simulation and experimental tests is used to develop a predictive model using artificial neural network (ANN) in MATLAB. 1000 data samples from a numerical model are used to train the ANN. Compared to experimental results, the ANN model predicts the designed benchtop system's total efficiency with a maximum efficiency of 51 % and an overall relative error of less than 15 %. This work will enable energy-efficient mission planning for UUVs using PCM-based ocean thermal energy harvesting. • Modeling of a benchtop hydraulic-to-electric energy conversion system. • The ANN predictive model uses the system pressure and electrical load parameters. • The maximum energy conversion efficiency is 51 %. • The system's total efficiency is predicted with <15 % relative error. • The prediction time (<1s) is suitable for UUVs where onboard energy is limited. [ABSTRACT FROM AUTHOR]

The characteristic curve of a pump-turbine is the essential data for calculating the hydraulic transient processes of a pumped storage power station. However, this curve is often unavailable during the preliminary design stage of pumped storage power stations. The shape of the characteristic curve directly influences the transient guarantee parameters. To date, adjusting the runner geometry to control the characteristic curve's shape for improving hydraulic transients remains a challenging issue. This paper proposes a model for predicting the characteristic curve that correlates its shape with the runner's geometric parameters. Considering the bidirectional flow in pump-turbines, two sets of governing equations are derived separately for the centripetal and centrifugal flow conditions. The governing equations for the unit discharge curve and the unit torque curve are constructed based on the energy equation and torque equation, respectively. Each term in these equations is subsequently organized into the expressions involving the runner's geometric parameters and unit parameters. To make the equations solvable, several sets of feature points are introduced. Based on the approach to obtain the feature points, an experiment/CFD (Computational Fluid Dynamics)-based prediction method and a statistics-based prediction method are proposed. Verifications using manufacturer-measured curves show that the experiment/CFD-based method is well-suited for model test acceleration and characteristic curve repair due to its higher prediction accuracy and availability, while the statistics-based method is more suitable for borrowing similar curve data during the power plant's preliminary design phase or rapidly predicting the corresponding characteristic curves during runner design. This work establishes a theoretical basis for improving hydraulic transient guarantee parameters by runner design optimization. • A novel prediction model for characteristic curves has been introduced. • Two methods are presented for the rapid prediction of characteristic curves. • A foundation for runner design to enhance hydraulic transient parameters. [ABSTRACT FROM AUTHOR]

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]

To improve the wave energy capture performance of a raft-type wave energy converter, a novel hybrid control strategy consisting of a passive control strategy and an active control strategy is proposed. In the passive control strategy, a nonlinear stiffness mechanism is introduced to generate a nonlinear passive control force. The passive control strategy has the advantage of broadening the capture width, whereas the active control strategy, which uses nonlinear model predictive control, is mainly utilized to improve the capture efficiency. Considering the high dimensions of the dynamic model, a dimension reduction strategy is given. Moreover, a mechanism for selecting the initial value is proposed to avoid the local optimum. Then, a dimension reduction nonlinear model predictive control controller is proposed to obtain the optimal active control force. Finally, the wave energy capture performance is evaluated for both regular and irregular waves. Numerical simulation results show that the wave energy capture performance can be improved using the novel hybrid control strategy. • A novel hybrid control strategy consisting of a passive control strategy and an active control strategy is investigated. • A nonlinear stiffness mechanism plays the role of phase control to achieve the inverse phase. • An initial value selecting mechanism is proposed to avoid the local optimum. • A dimension reduction strategy is proposed to enhance real-time performance. [ABSTRACT FROM AUTHOR]

Demand response capacity is a key reference for demand-side participation in peak regulation and earning profit in the electricity market, and it is influenced by not only external public factors but demand-side internal individual factors. Demand change caused by internal individual reasons cannot be recognized by public features. Single participating in response industrial and commercial consumers cannot balance this change through large numbers of participants, unlike aggregators. The prediction model only considering the external public factors will cause significant errors. However, the actual industrial and commercial data is difficult to obtain because of the cost and privacy, and the simulation platform cannot accurately describe this situation, resulting in the problem being ignored. Therefore, this paper aims to prove the importance of internal factors and proposes an improved method considering the problem. First, based on an actual dataset, the optimal responsive behavior of consumers is modeled to quantify response capacity. Second, a support vector machine (SVM) method is applied to predict response capacity. Finally, combined with raw data, this paper proves that significant errors are caused by internal factors and provides different improvement methods for internal and external operators. The case study indicates that the improved model can provide better performance. • The impact of internal factors on response capacity prediction is proven, and prediction accuracy has been improved. • A method is proposed to improve the prediction accuracy for operators who cannot directly obtain internal information. • Based on the actual dataset, optimization and simulation of user behavior can better reflect the real response situation. • This study is developed under the price-based and incentive-based composite demand response program. • This study improves the modeling method of HVAC. [ABSTRACT FROM AUTHOR]

The present research on photovoltaic (PV) forecasting is devoted to the use of spatial information about PV sites to improve the accuracy of the models, but most of the models have to increase their network complexity to learn the spatial dependence. In this paper, we propose to take advantage of the known geographic location information of PV sites and embed them directly into the input information of Decoder, which makes it easier for the model to focus its attention. We refer to this process as relevance amplification. Based on this, this paper proposes Relevance Amplification based DeepFEDformer (RAD-FEDformer), where DeepFEDformer adds multiple Multi-Layer Perceptron (MLP) layers to FEDformer to improve the perception of deep features. The Relevance Amplification Module (RAM) is designed to receive geographic correlation information as a way to enhance its influence on the Seasonality component of the Decoder input and improve the performance of the attention mechanism in the model. Using the power generation data from PV plants distributed in 11 regions of Belgium as our case study, we evaluated the performance of RAD-FEDformer in predicting PV data at 48/96/192 time steps into the future for each plant. Compared to other Transformer family models, RAD-FEDformer achieved SOTA results by demonstrating significant R 2 improvements of 16.70%, 51.45%, and 23.50% over FEDformer, along with an average MSE reduction of 16.9%. We designed Ablation Experiments to validate and compare the performance of MLPs of different sizes based on the ETTm1 dataset and discussed the performance of RAM and its effect on the attention mechanism based on the PV dataset. The results show that our model enhances the performance of the attention mechanism in Multi-Site PV prediction scenarios, and conclude that the optimization of RAM is more effective in longer sequence predictions. • Propose a module for embedding geographic information into input data with low overhead. • Improving the FEDformer model using Multi-layer Perception. • Discuss the performance of the model on multivariate prediction task based on ETT and PV datasets. • Compare several popular Transformer family forecasting models in the case study. [ABSTRACT FROM AUTHOR]

Startup and shutdown are quite common processes in the operation of fluid machinery. Therefore, it is of great significance to clarify the mechanism of pumps during start-up periods for their safe and stable operations. To this end, the transient performance of a centrifugal under start-up conditions is investigated experimentally and theoretically. A theoretical prediction model is established based on the pipeline balance between the transient pump head and pipeline resistance, combined with the corresponding time-stepping algorithm for solving the pipeline balance equation. The independence test of the transient time step for the prediction method is conducted to determine a proper value. The predicted results show that sufficient prediction accuracy can be achieved, with an average relative error of 11.13 % compared with experimental results. A thorough mathematical analysis is performed to demonstrate the convexity of the characteristics of the transient pump head according to its second derivative from the pipeline balance equation. Then, a unified hyperbolic sine function with a normalized transient pump head and normalized time is established, which works well for the same pump under start-up conditions with various acceleration time. • A theoretical model is established and applied to predict the transient performance of a centrifugal pump. • A unified hyperbolic sine function is established to model the time evolution of the transient pump head. • An average relative error of 11.13 % for theoretical model in comparison of experimental results. [ABSTRACT FROM AUTHOR]

The availability of sample data, required for seismicity-based reservoir prediction using deep learning (DL), is often limited by well availability. To alleviate this problem, this study proposes a hybrid DL method combining unsupervised learning, transfer learning (TL), and intelligent optimization algorithms to predict gas-bearing distributions. First, three unsupervised learning methods were used to optimize multicomponent seismic attribute data during preprocessing and improve the sample data quality. Subsequently, a convolutional neural network (CNN) model was pre-trained using synthetic data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm was used to optimize the hyperparameters of the CNN model during training to obtain the AMPSO-CNN model. Finally, the partial convolutional layer parameters of the pre-trained network were transferred, and the remaining parameters were adjusted using real data samples to obtain the TL-AMPSO-CNN model. The trained model was used for gas-bearing distribution prediction, and its stability was assessed using uncertainty analysis. The results confirmed that the hybrid DL model performed efficiently in gas-bearing prediction compared to other individual models. Thus, for datasets with insufficient training samples, the proposed scheme can be applied as a feasible alternative to predict gas-bearing distribution. • A hybrid method based on CPICA-TL-AMPSO-CNN is proposed for gas-bearing prediction. • CPICA is used to optimize multicomponent seismic data to obtain high-quality samples. • Transfer learning and AMPSO are used to improve the prediction performance of CNN. • The proposed hybrid DL model has better performance than other prediction models. • The internal and data uncertainty analysis shows that the proposed model is robust.. [ABSTRACT FROM AUTHOR]

In the field of oil production, accurately predicting well production is of great significance for optimizing production processes and enhancing economic benefits. However, oil production data generally exhibit characteristics such as high noise, complex distribution, and severe temporal fluctuations, which pose significant challenges to traditional prediction models, thereby limiting their accuracy. To address this issue, this study proposes an innovative Time Patch Dynamic Attention Transformer (TPDAT) model. This model segments the time series data into multiple time patches, extracts local features, and integrates a dynamically fused causal temporal attention mechanism to enhance the recognition of transient events and short-term fluctuations. The TPDAT model comprises the Temporal Patch Feature Extraction (TPF) module and the Dynamic Fusion Causal Temporal Attention (DFCTA) mechanism, effectively capturing multi-scale temporal features and enhancing the modeling of causal relationships during oilfield production. Experiments on the North Sea Volve oilfield dataset show that the TPDAT model significantly outperforms traditional deep learning models in well production prediction, achieves the best performance in model evaluation metrics, and demonstrates strong adaptability to complex data. The results confirm that the TPDAT model improves the accuracy and stability of well production prediction in complex oilfield environments, providing reliable support for oilfield production optimization. • TPDAT model merges temporal feature extraction and dynamic attention to enhance oil field production forecast accuracy. • TPDAT outperforms traditional deep learning models on Volve dataset, reducing prediction error by 20-30% on average. • TPDAT excels in handling noisy, high-dimensional oilfield data, suitable for complex geological conditions. • TPDAT shows strong generalization, adapting to wells with varying features, supporting reliable oilfield management. [ABSTRACT FROM AUTHOR]

Heat-exchange station is the key mid-link between heat source and heat users in district heating system, playing a role in distributing heat and regulating supply and demand. Accurate load prediction at the level of heat-exchange stations and then regulating the heat supply is the important step to optimize district heating system. Based on real and limited data, the framework of heat-exchange station load prediction method is established in this study, which includes 5 procedures 'data preprocessing method, input variable selection, solar radiation substitution, prediction model establishment and model transplantation application'. The sliding box plot method was proposed for data preprocessing, and the effects of data preprocessing under the widths of 24 h, 48 h, 72 h, 96 h and 120 h were compared, with 48 h as the optimal sliding window width. Support Vector Regression (SVR) algorithm was used to establish a heating load prediction model for heat-exchange station in residential buildings. And parameter substitution was made for solar radiation data that cannot be obtained in weather forecasts. High accuracy was obtained with a MAPE of 5.80 % and RMSE of 0.18 GJ. The above model was transplanted and applied in three other heat-exchange stations of different building types. The MAPEs were 5.51 %, 7.79 % and 6.54 %, with corresponding RMSEs of 0.017 GJ, 0.04 GJ and 0.04 GJ, respectively, which all achieved good prediction results. The research results provide certain technical support for the operation and regulation of district heating systems. • Data preprocessing of the raw heat data was preprocessed using sliding box plots and an optimal window width 48h. • Analyzed the influencing factors of the model and selected the optimal 9 input variables. • Proposed a substitution method for solar radiation. • Using SVR algorithm, a load prediction model was built in 4 different heat-exchange stations and good prediction results were achieved. [ABSTRACT FROM AUTHOR]

The community integrated energy system (CIES) can provide users with reliable and economical energy supply through centralized utilization of various energy equipment. However, due to the significant difference in inertia between different energy sources, the response speed of equipment using different energy sources varies greatly, making it difficult to cooperate well and limiting their ability to cope with source-load uncertainty. Based on this consideration, a two-stage optimal operation strategy for CIES is proposed to utilize dynamic and static energy separately. First, the dynamic and static energy are defined and divided, and their different functions are analyzed. Based on this, a two-stage optimal operation architecture is proposed. Then, a day-ahead interval optimal operation model considering dynamic and static energy matching is established. In which, the uncertainty of source and load is characterized by nonlinear interval numbers, and the interval possibility degree is used for deterministic transformation of nonlinear interval numbers. Thirdly, a dynamic and static energy replacement mechanism based on model predictive control is given, and an intra-day rolling optimal operation model considering dynamic and static energy replacement is established. Finally, a CIES is used as an example to demonstrate the rationality and effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively utilize different types of energy equipment and significantly reduce the adverse effects of source-load uncertainty. • The dynamic and static energy are separated and applied into two-stage optimization. • Interval determinacy method is extended to make more universal. • The concept of dynamic and static energy replacement is proposed. [ABSTRACT FROM AUTHOR]

Precise prediction of PV power in the short term is crucial for maintaining power system stability and balance. However, the performance of conventional time series prediction models on short-term long series prediction is scarcely sufficient because of the stochastic and turbulent character of PV power data. This work suggests a PV short-term power forecast model based on weather type, AHA-VMD-MPE decomposition reconstruction, and improved Informer combination to tackle this issue. Firstly, a SUM-ApEn-K-mean++ multidimensional clustering method to group the dataset by weather conditions. Then an AHA-VMD-MPE decomposition model is proposed to decompose the historical power data Finally the Informer model is improved and the improved model is utilized to predict the PV power under various weather conditions. The model exhibits great accuracy and stability in short-term PV power prediction, as demonstrated by the experimental results, which were validated using measured data from many PV power plants. • A weather typing method based on SUM-ApEn-K-means++ has been proposed. • A sequence decomposition method based on AHA-VMD-MPE has been proposed. • The improved Informer model has been applied to short-term PV forecasting. • Validation has been performed on multiple time scales. [ABSTRACT FROM AUTHOR]

This research aims to improve the performance and economics of fuel cell hybrid electric vehicles (FCHEVs), validated and established by introducing an innovative energy management strategy (EMS) based on a speed-predictive fusion model. Firstly, a mixed prediction model was built based on BiLSTM, TCN, and Self-attention (SA) mechanism to accurately search, capture and fuse multi-granularity features in time series. Then, Harris-Hawk Optimization (HHO) was used to optimize the dropout rate and model learning rate of the combined BiLSTM-TCN-SA time series model to improve the prediction accuracy and generalization ability of the model. Finally, stochastic model predictive control was combined with BiLSTM-TCN-SA to form SMPC-NSGA III algorithm, which was used for multi-objective optimization of fuel economy, fuel cell durability and battery durability. In this study, the effectiveness of the proposed strategy was verified under the condition of CLTC-P driving cycle. The experimental results showed that RMSE and R2 of HHO-BiLSTM-TCN-SA velocity prediction model are 1.169 and 0.998, respectively. In addition, the output of the model is within the confidence interval of 97.5 % of the real speed, and there is no significant difference, which is statistically significant. Under the SMPC-NSGA III strategy, the average efficiency of the fuel cell was increased by 12 % and 1 % respectively. • A new energy management strategy was created to improve fuel cell durability. • HHO-based optimisation of BiLSTM-TCN-Self-Attention hyperparameter fusion model improves speed prediction performance. • NSGA-III multi-objective optimization algorithm was combined with SMPC for collaborative optimization results. • The average efficiency of the fuel cell in the SMPC strategy is higher than the DP and FLC. [ABSTRACT FROM AUTHOR]

Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. • The algorithm was validated using both NASA and CALCE datasets. • Improving the dung beetle optimization algorithm using three strategies. • The superiority, robustness and stability of the algorithm are verified. [ABSTRACT FROM AUTHOR]

Numerical simulation techniques have become an important tool in the study of thermal runaway (TR) in lithium-ion batteries (LIB). With the research progress, the complexity of the TR prediction model increases, extending its application from single battery to battery packs and entire vehicle systems. This expansion has led to a significant increase in the computational demands of TR prediction model. The computational efficiency has become a critical issue that cannot be ignored. Therefore, it is meaningful to research and develop accelerated computational strategies for the TR prediction model while ensuring the computational accuracy and stability. This paper introduces specific accelerated computational strategies to tackle this challenge. The stability and accuracy of these strategies are validated using 2D and 3D models developed with OpenFOAM software, and the speed up effect from coupling of different accelerated schemes is also analysed. The results show that, compared with the traditional prediction model, coupling various accelerated strategies can speed up the 2D model by a factor of 4.91x and 3D model by a factor of 6.07x. The optimized models with accelerated computational strategies substantially improve the ability to handle complex working conditions, large-scale models and TR propagation problems. • Multi-scale, multi-physics coupled thermal runaway prediction model. • Thermal runaway process visualization experiment. • Various model acceleration strategies. [ABSTRACT FROM AUTHOR]

This study proposes a novel prediction model to accurately quantify the carbon emissions of the trucks driving on dirt roads based on the deep learning neural network for small sample (DNNSS) and the motor vehicle emission simulator (MOVES). The application range of the MOVES model was extended to the transportation of asphalt mixtures on the temporary road. The model correction method was also established based on the rolling resistance coefficient (C R) and the correction coefficient (μ). By comparing the measured fuel consumption of the truck with the carbon emissions calculated by the MOVES model, the values of C R × μ can be back-calculated. DNNSS was constructed for estimating the C R × μ. The Adaptive Moment Estimation (Adam) algorithm was used to dynamically adjust the learning rate and accelerate the convergence of the network; the Dropout function was used to alleviate overfitting; and the Rectified Linear Unit (ReLU) function was used as the activation function to solve the gradient vanishing problem. The test results showed that the vehicle speed and load greatly influence the C R × μ. The DNNSS algorithm was better at predicting the C R × μ. The proposed DNNSS-MOVES model was more accurate than the conventional methods. • New method for estimating the carbon emissions of trucks was developed. • The C R × μ was quantified using the back-calculation method. • The DNNSS algorithm was used to calibrate the MOVES model. • The DNNSS-MOVES model was more suitable for predicting carbon emissions of trucks. [ABSTRACT FROM AUTHOR]

With the promotion of low-carbon models, the proportion of wind power energy has significantly increased. Accurate wind power forecasting is of great significance for the scheduling of power systems. Previous studies often focused on improving forecasting accuracy, neglecting the issue of model failure (abnormally large forecast error occurring). However, forecasting model failure brings significant misleading information to the scheduling of the power system. To address this issue, this paper firstly analyzes the error distribution of prediction models under various neural networks (CNN, CNN-GRU, DNN, ConvLSTM, ELM, GBDT, AR, TREE and XGBoost) and various loss function (MAE, MSE, MLSE and Log-cosh) combinations. Subsequently, based on the Backward cloud generator and Pearson correlation analysis, the paper confirmed that the forecasting errors mainly come from the volatility of the wind power sequence itself, rather than the types and structures of the models. Finally, the paper uses variation and variance to assist Bi-LSTM in 1-h-ahead early warning of forecasting model failure under two kinds of thresholds, and has achieved excellent reliability and accuracy. The warned models show obvious failure situations, both in terms of single error peaks and cumulative errors. • The paper advocates studying the failure of forecasting models. • Multiple neural networks and loss functions are applied. • The main sources of forecast errors have been identified. • The relationship between original variation and forecast error is analyzed. • A method about early warning for forecast model failure is proposed. [ABSTRACT FROM AUTHOR]

Establishing an accurate and stable NOx concentration prediction model is the foundation for achieving environmental protection in coal-fired power plant denitrification. In this research, a data-driven hybrid prediction model (BMFE-MFST-GNN) is proposed to improve the prediction accuracy of inlet NOx concentrations. First, we develop the adaptive variational modal decomposition method (FEVMD) to decompose historical NOx concentration data into simple and smooth subsequences and extract time-frequency features. Second, we propose the boosting mutual information feature selection algorithm (BMIFS) to determine the best set of auxiliary variables. The delay time is calculated based on the maximal information coefficient (MIC) to reconstruct the datasets. Then, the multi-channel fused spectral temporal graph neural network (MFST-GNN) is created to build the graph feature information of decomposed subsequences and reconstructed auxiliary variables to predict the concentration subsequences. Finally, we integrate the subsequence prediction results to obtain the future NOx concentrations. The experimental results demonstrate that the proposed method outperforms several comparative models in predicting NOx concentrations. [ABSTRACT FROM AUTHOR]

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades. • A blade degradation performance fast prediction model based on BPNN is proposed. • The effect mechanism of degradation/regulation condition parameters is revealed. • The rapid multi-objective optimization model of operating parameters is developed. • The real-time performance prediction and status rapid optimization are realized. [ABSTRACT FROM AUTHOR]

The inefficient operation of air-conditioning system, which leads to its high-energy consumption, has aroused far-ranging concern at present. And an accurate prediction of building cooling load can be conducive to matching the supply and demand of the air-conditioning system, thus improving its operational efficiency. Therefore, a hybrid forecasting model (BAS-GRNN&LSTM) combing generalized regression neural network (BAS-GRNN) and long short-term memory neural network (BAS-LSTM) optimized by beetle antennae search algorithm is proposed for building cooling load prediction. First, the factors affecting the cooling load are comprehensively analyzed, and the random forest combined with recursive feature elimination (RF-RFE) method is used for feature selection. Then, the BAS-GRNN&LSTM model is developed to forecast. Finally, a simulation experiment is carried out using the measured data of a large building in north of China. Compared with GRNN, LSTM, BAS-GRNN and BAS-LSTM, BAS-GRNN&LSTM performs better in five performance evaluation metrics, RMSE, MAPE, RRE, MBE and R2, demonstrating its higher prediction accuracy. Furthermore, the detailed algorithm performance analysis shows that the proposed hybrid model has significant advantages in robustness, generalization capability and computational complexity, and can be well applied to building cooling load prediction and beneficial to the optimal control of building air-conditioning system. • Random forest combined with recursive feature elimination method is used for the feature selection of prediction model. • The network parameters of GRNN and LSTM are optimized using the BAS algorithm to make them in the optimal prediction state. • BAS-GRNN&LSTM forecasting model is developed for building cooling load forecasting. • The prediction performance of BAS-GRNN&LSTM model is deeply verified and analyzed by comparing with other models. [ABSTRACT FROM AUTHOR]

Previous prediction models have faced challenges in characterizing the three-dimensional (3D) surface subsidence of salt caverns with different shapes. In order to overcome these limitations, this study proposes a novel theoretical model based on stochastic medium theory. This model provides a theoretical formula for characterizing the 3D surface subsidence of salt caverns with different shapes (oblate spherical, spherical, and vertical ellipsoidal). By employing the Particle Swarm Optimization (PSO) algorithm to address the theoretical model, it can generate a three-dimensional settlement surface and subsequently compare it with the numerical results. The results demonstrate a strong consistency between the theoretical and numerical results. By conducting the parameter sensitivity analysis, it becomes evident that the shape of salt caverns holds paramount significance and should not be disregarded in the study of 3D surface subsidence. As the main influence angle increases and the burial depth decreases, the maximum settlement value of the salt cavern continues to increase while the maximum influence radius continues to decrease. The maximum settlement value and maximum influence radius of salt caverns gradually increase with the increase of the span. This suggests that the proposed theoretical model can effectively and intuitively describe the 3D surface subsidence characteristics of salt caverns. • A theoretical model of 3D surface subsidence has been proposed based on stochastic medium theory. • The proposed model can accurately predict 3D surface subsidence of salt caverns with different shapes. • The proposed model can effectively describe the 3D surface subsidence characteristics of salt caverns. [ABSTRACT FROM AUTHOR]

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting. • CEEMD technology reduces the complexity of data samples. • The combine prediction model effectively improves the prediction accuracy. • Use grey relation analysis technique to determine the input of prediction model. • The improve sparrow optimization algorithm is utilized for parameter optimization. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings. • A model predictive control methodology for school buildings was presented. • The methodology was applied to a fully electric school building near Montréal, Canada. • Data-driven grey-box models were developed and calibrated using measured data. • The model predictive control provided between 47% and 95% energy flexibility during on-peak hours. • The model predictive control reduced the cost of electricity by 44%. [ABSTRACT FROM AUTHOR]

Accurate electricity consumption forecast plays an important role in managing and operating a country's power system and is a challenging task due to the data exhibiting the mixture characteristics of trend, periodicity and randomness. To accurately describe the dynamic changes of seasonal fluctuations of electricity consumption, a novel discrete time-varying grey Fourier model with fractional order terms is proposed by coupling Fourier functions with fractional order time-varying terms, as the grey action, to fit the amplitude variations hidden in seasonal pattern and improve the fitting and forecasting accuracy. Moreover, the mechanism and properties of the proposed model are discussed. Then the optimal fractional order is determined by the Dingo Optimization Algorithm (DOA) and the optimal Fourier order is selected by the hold-out method to enhance the robustness of the model. Subsequently, the results of designed experiments based on Monte-Carlo method testify the validity of the Fourier order selection method and the proposed model. Finally, this model is applied to predict the monthly residential electricity consumption of China from 2015 to 2022. The results indicate that the proposed model captures the dynamic amplitude variations of the time series and has better prediction performances than other benchmark grey prediction models and non-grey prediction models. • The new model aims to simulate seasonal time series with amplitude variations. • The hyper parameters are given by Dingo Optimization Algorithm and hold-out method. • The experiments based on Monte-Carlo method testify the validity of the model. • Monthly residential electricity consumption forecast is discussed. • The new model has higher prediction accuracy compared with five benchmark models. [ABSTRACT FROM AUTHOR]

Casing damage is one of the common problems encountered in reservoir development, which seriously affects the normal production of the oil field. In this study, through the analysis of oil field data, a casing damage model under the coupling effects of mudstone hydration-corrosion and sand production-corrosion was established. Thirty-four influencing factors of casing damage were determined in four categories: geology, engineering, development, and corrosion. Six machine learning methods were used to predict the probability of casing damage under the coupling effects of multiple factors. The generalization performance of the model was evaluated using the recall rate of casing damage wells and accuracy. The results show that the random forest and LightGBM models show the best generalization performance. The prediction accuracy rates of the two models on the test set were 84.2% and 85.9%, respectively, and the random forest model showed an excellent performance of 92.3% on the recall rate of casing damage wells. Finally, the optimized model was used to perform sensitivity analysis on each influencing factor, and the main controlling factors of casing damage were obtained. Engineering measures to prevent casing damage are proposed. This study has made outstanding contributions to improving the economic benefits of the oilfield. [Display omitted] • A machine learning method for casing damage prediction under multi-factor coupling conditions was established. • The proposed casing damage prediction model can accurately predict the downhole casing's real condition. • Several engineering measures were proposed to reduce the probability of casing damage for new and old wells. • Considered geological, engineering, development, and corrosion factors that affect casing damage. [ABSTRACT FROM AUTHOR]

The safe and stable operation of electric vehicles relies on fast and accurate predictions of the state of health (SOH) of the battery. To address challenges such as limited availability of extensive battery aging data or data with informative missingness, the novel SOH prediction method based on the improved method whale optimization algorithm (IWOA)-Bi-directional Long Short-Term Memory (BiLSTM) with strong correlated single aging feature is proposed. Firstly, to accurately predict the accelerated degradation process of the battery capacity, the knee-point in the capacity degradation curve is identified as a starting point for SOH prediction by Bacon-Watts model. Next, a small number of early partial aging features of the battery cycle are extracted, such as time of charging or discharging, and various correlation analysis methods are used to select the single feature with the highest correlation with capacity degradation to reduce the computational complexity of multiple feature factors. Finally, BiLSTM model is established to predict battery SOH. In addition, in order to improve the efficiency of the adjustment for hyperparameters, IWOA is proposed to optimize the BiLSTM's hyperparameters. Compared to the traditional Whale Optimization Algorithm (WOA), IWOA has better global search capability, robustness, and efficiency through enhancements in search strategy, mutation operation, adaptive parameter adjustment, and performance optimization. The proposed method is validated using battery datasets from NASA and CALCE. Compared with BiLSTM and WOA-BiLSTM, the simulation results indicate that the MSE of SOH prediction based on IWOA-BiLSTM method mostly remains below 0.05, and index of agreement (IA) basically maintains higher than 99%. • Identifying the knee-point in battery degradation curve as a fresh starting point for SOH prediction. • Strongly correlated single feature is utilized to improve computational efficiency of predictive model. • Enhancing the performance of WOA through four improvement strategies. • Bi-LSTM with IWOA for accurate and robust health prognostic. • The effectiveness is verified on batteries with different degradation patterns and incomplete partial data. [ABSTRACT FROM AUTHOR]

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control. • Built an LSTM-based MPC for energy management on oil-cooled motors in HEV. • Constructed an empirical model for oil-cooled motors. • Proposed a novel metaheuristic algorithm for solving optimization problems in MPC. • The time cost of the algorithm is the lowest among metaheuristic algorithms. • The algorithm showed minimal correlation between runtime and operating conditions. [ABSTRACT FROM AUTHOR]

Accurately predicting gas load is crucial for optimal planning and scheduling of natural gas production. Existing machine learning or deep learning-based prediction methods primarily aim to improve accuracy. However, these methods face challenges such as overlapped features, significant background noise, and residual errors. In response to these challenges, we proposed a fusion prediction model, known as the Double Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) with Three-way Residual Error Amendment (DT-LGBM-3WREA). To address the overlapped feature issue in time series data, we utilize the STL module for data decomposition. To handle background noise, the network incorporates a double TCN module specifically designed for trend and seasonal components. Thus, it can better avoid the transmission of noise between modules. To address residual error, we introduce three-way decision methodology and develop the 3WREA module, which aims to avoid local optima and random factors from perturbing the prediction accuracy. This approach mitigates the influence of remainder item on overall prediction performance. Experimental results demonstrate that the DT-LGBM-3WREA model excels in gas load prediction scenarios, surpassing state-of-the-art methods. • The three-way decision (3WD) is introduced to the gas load prediction problem. • A three-way residual error amendment (3WREA) fusion model is proposed. • With our model, improvements of 52.15% in MAE & 53.69% in RMSE are verified. [ABSTRACT FROM AUTHOR]

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O 2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO 2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine. [Display omitted] • The temperature and gaseous product exhibit a nonlinear relationship during CSC. • After 100 °C, the gas generation rate spikes notably in coal near the inlet. • CSC temperature is predicted by gas concentrations and distance from air inlet. • The temperature prediction model proposed achieves accuracy of 0.94997. [ABSTRACT FROM AUTHOR]

Accurate energy consumption prediction is pivotal in supporting efficient grid scheduling and enhancing renewable energy penetration. This paper proposes a novel hybrid prediction model called CEEMDAN-SE-EC-BiLSTM. The proposed model utilizes Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the original energy consumption data into Intrinsic Mode Functions (IMFs). Afterwards, Sample Entropy (SE) is employed to evaluate the complexity of the IMFs. The complexed IMFs are then transformed into simpler IMFs using a second decomposition. All IMFs are predicted using Bidirectional Long Short-Term Memory (BiLSTM) to attain the initial predicted sequence. To further refine the prediction, an Error Correction (EC) technique is introduced. This involves subtracting the initial predicted sequence from the original sequence to obtain the error sequence, which is then decomposed using CEEMDAN. Subsequently, the decomposed error sequence is predicted using BiLSTM to generate the error prediction sequence. Finally, the predicted results and the error prediction are combined to obtain the final energy forecast sequence. To verify the superior performance of the proposed method, the model is tested on five selected buildings and benchmarked against seven prediction models. The experimental results indicate that the proposed hybrid prediction model achieves the highest accuracy in building energy prediction. • A novel hybrid approach for building energy prediction is proposed. • The SE is used to evaluate the necessity for secondary decomposition. • The EC technique is employed to improve the prediction accuracy. • The performance of the proposed model is benchmarked against other models. [ABSTRACT FROM AUTHOR]

Accurate and robust wind power forecasting (WPF) is great significance to ensure the safe and stable operation of the power system and promote the transformation of low-carbon energy. However, the high randomness and intermittency of wind power bring great challenges when designing reliable forecasting models. In this paper, a novel spatial-temporal attention graph convolutional network model is proposed. Firstly, the spatial attention mechanism is used to aggregate and extract the spatial correlations of the raw wind power data. Secondly, the temporal attention mechanism is applied to capture the temporal correlations. Then, the extracted spatial-temporal correlations were put into the temporal convolution network and the spatial convolution network to further obtain the temporal and spatial dependencies. Finally, the wind power forecasting results is output through the full connection layer. The proposed method is verified by using wind power data from real wind farm in China. The experimental results reveal that the proposed depth spatiotemporal prediction model has more significant advantages than other advanced models in terms of prediction accuracy and stability. • The spatiotemporal attention mechanism is used to learn the dynamic spatiotemporal correlations. • The spatiotemporal convolution mechanism is applied to enhance feature extraction of wind power. • The proposed model can improve the accuracy of wind power prediction by experiments. • Real datasets from wind farm in China is employed for the modeling study. [ABSTRACT FROM AUTHOR]

To meet the active regeneration requirements of the diesel particulate filter (DPF), the outlet temperature of the diesel oxidation catalyst (DOC) must be maintained within the range of 585 °C–615 °C. Firstly , this paper explores the DOC system using a set of partial differential equations that describe the coupled flow and heat transfer of both the gas phase and solid phase carriers. Subsequently, the DOC model is significantly simplified, and the equations are solved based on their characteristics. Secondly , a Luenberger observer is designed, and the appropriate range of values for the observer gain is calculated. By minimizing the cost function and adhering to constraints within a finite prediction horizon, the optimal sequence for injecting hydrocarbon(HC) is determined. Thirdly , in this study, a Worldwide Harmonized Transient Cycle test is conducted on the engine, following the China VI emission standard. The computational results demonstrate that the mean absolute errors of the DOC outlet temperatures obtained from the model prediction and the observer are 0.535 °C and 1.994 °C, respectively. Finally , a nonlinear model predictive control is implemented to ensure that the DOC outlet temperature can be maintained between 585 °C and 615 °C. [Display omitted] • The DOC gas phase and solid phase temperature differential equations were simplified. • The Backward Differentiation Formulas method was used to solve the DOC model and the parameters were identified by the Levenberg-Marquardt algorithm. • The Luenberger observer was designed and the Lyapunov function was constructed to prove its stability. • The DOC model was combined with the minimised cost function and NMPC was implemented to optimise the HC injection strategy. [ABSTRACT FROM AUTHOR]

Forecasting bioenergy feedstock commodity volatility has received significant attention due to its importance in biofuel production and household consumption. Several extreme events, including the COVID-19 pandemic, have sparked interest in studying the role of structural breaks on volatility modeling and prediction in these markets. This study extensively examines the prediction performance of econometric models at multiple horizons using a rolling-window approach, with and without accommodating structural changes. We exploit the ICSS algorithm to determine the in-sample estimation windows to accommodate structural breaks. We extend the procedure beyond GARCH-class models. Also, the detected break information defines the regime dummies. The study innovatively evaluates the prediction performance of specific GARCH-class models by incorporating binary variables for sudden shifts in unconditional variance. Our findings reveal that accounting for the endogenously detected structural breaks through the dummy variables leads to considerable forecast accuracy gains. • This paper focuses on modeling and forecasting the volatility of bioenergy energy crop markets. • We thoroughly examine different econometric models with and without accommodating structural breaks at multiple horizons. • Some models rely on the adjusted ICSS procedure to determine the size of the estimation windows and define regime dummies. • Accounting for the structural breaks results in considerable accuracy gains in point and direction forecasts. [ABSTRACT FROM AUTHOR]

Biodiesel is a good alternative to fossil fuels for conventional engines, but determining the properties of biodiesel can be a time-consuming and resource-intensive process. Therefore, the development of models capable of predicting these properties would be of great importance. In this work, different machine learning models were investigated for predicting the Iodine Value (IV) based on the distribution of fatty acid methyl esters (FAME). For this purpose, a database with 266 examples of biodiesel from different feedstocks (1st, 2nd and 3rd generation) was used along the leave-one-out methodology. The main results of the work are: the double bonds and the distribution of FAMEs are the best attributes for predicting IV and the XGBoost algorithm gives an absolute mean error of 11.4 units; the machine learning models for predicting biodiesel properties need to be trained on a large number and variety of biodiesel examples to better predict and generalize; the use of both ANNs and the hold-out approach of dividing the dataset into train/validate/test are not recommended due to the risk of overfitting and the algorithm's dependence on which examples form each group given the currently available data. The leave-one-out method is most appropriate for estimating model performance. • Double bonds and FAMEs distribution are the best attributes to predict IV. • The XGBoost algorithm gives the best results when predicting IV. • Models must be trained on a large number and variety of biodiesel examples. • The use of ANNs is at risk of overfitting given the data currently available. • The train/validate/test approach also perform bad with the current data. [ABSTRACT FROM AUTHOR]

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information. • Speed prediction based on real driving data. • Comparing the accuracy of different speed prediction methods in different prediction time domains. • Fusion of multivariate information (vehicle history, front vehicle speed, distance, signal light status, remaining time, and other factors) for speed prediction. [ABSTRACT FROM AUTHOR]

Wind speed prediction methods used to schedule wind power generation in advance is of great significance for ensuring grid safety and improving wind energy availability. However, most existing wind speed prediction models insufficiently extract spatial features for predicting wind speed at wind turbines stations, leading to less satisfying prediction results. To solve this issue, an adaptive spatiotemporal features fusion Transformer is proposed based on graph attention network (GAT) and optimizable graph matrix. First, a novel parameter optimization matrix is constructed using geographic information of cluster wind turbines, dynamic time warping (DTW), and maximum information coefficient (MIC) information to express the wind speed spatial correlation among these turbines. Second, graph attention network is used to extract spatial features from this matrix, sufficiently evaluating spatial similarity of wind speed series at different stations. Third, an embedding block is performed to characterize temporal features of cluster wind speed. Fourth, to effectively integrate these spatial and temporal features into spatial-temporal features, a new type of entangle block based on parameter optimization is proposed. Fifth, predicting values are obtained based on an improved Transformer by extracting effective features from spatiotemporal features with multi-head attention mechanism. Finally, Huber loss function is used to iteratively optimize the network parameters of the proposed model. To verify the effectiveness of the proposed model, five performance indexes, including MAE, MSE, MAPE, TIC and IA are employed. The results show that the proposed model outperforms other models including GAT-GRU, GAT-LSTM, GAT-Transformer, GCN-GRU, GCN-LSTM, GCN-Transformer, and Autoformer. • An adaptive features fusion Transformer is proposed to predict cluster wind speed. • An optimization graph matrix is constructed to represent spatial correlations. • A novel entangle block is built first to integrate spatial and temporal features. • The performance of GAOformer is superior to seven common models. [ABSTRACT FROM AUTHOR]

Accurate and reliable cooling load forecasting is a prerequisite for air-conditioning system control and the basis for building-side energy management. Therefore, a hybrid prediction model of an improved bidirectional long short-term memory (BiLSTM) network based on principal component analysis network (PCANet) and attention mechanism (CNN-IBiLSTM-Attention) is proposed to predict the cooling load of large commercial buildings. First of all, the PCANet algorithm is used to analyze the sensitivity of the influencing factors. Then, the hybrid strategy improved whale optimization algorithm (HSIWOA) is used to optimize the hyperparameter of BiLSTM. At last, the performance of the proposed algorithm is verified by using the actual data of two commercial buildings in Xi'an. The results show that using the PCANet algorithm for sensitivity analysis avoids feature redundancy. HSIWOA is suitable for hyperparameter optimization of BiLSTM. Compared with the other three prediction models, CNN-IBiLSTM-Attention reduced the mean absolute percentage error (MAPE) of Building 1 and 2 test sets by 31.55 %, 55.59 %, and 60.58 % and 56.49 %, 60.3 %, and 67.37 %, respectively. The proposed prediction model has superior hyperparameter optimization ability, better model complexity, and stronger generalization ability. Therefore, the proposed prediction model becomes a reliable tool for predicting the cooling load of large commercial buildings. • Comprehensive classification of factors affecting commercial building cooling load. • Feature extraction method based on the PCANet algorithm. • Combining PCANet, BiLSTM, and attention mechanism for cooling load forecasting. • Using the HSIWOA to optimize the hyperparameters of BiLSTM. • Verifying the validity of the CNN-IBiLSTM-Attention model using actual data. [ABSTRACT FROM AUTHOR]

Wind turbine wake prediction considering yawed condition is of great importance for wind farm applications. Tremendous efforts have been made on yawed wind turbine wake prediction by using analytical wake models and numerical simulations, while their applications are still limited due to a lack of balanced consideration on both efficiency and precision. In this work, a deep learning-based wake prediction model is developed by integrating the transformer module into the conditional generative adversarial network. The developed model takes the inflow velocity and turbulence field as the input to predict the three-dimensional wake flow under different yawed conditions. The data generation approach by both analytical and numerical ways is used to build a large wake database. Subsequently, a pretraining-finetuning strategy is adopted to improve the model training efficiency and enhance the prediction performance. The validation results show that the proposed model can achieve a good agreement with numerical simulations at different streamwise distances under various inflow conditions, with the mean absolute relative error of 5.0 % and 7.27 % for wake velocity and turbulence intensity, respectively. The model parameters are also investigated to illustrate the wake prediction improvement by transformer-mixed modelling method. The wake prediction performance of the proposed model is validated by a comparison with analytical wake model and other popular machine learning-based methods. Moreover, the power calculation of multiple wind turbines is conducted to demonstrate the easy implementation and good performance in wind farm applications. • A novel three-dimensional yawed wake prediction model is developed based on transformer module. • The transformer-mixed model can accurately predict the wake flow field and generalizes well. • The wake prediction performace of different machine learning-based methods are compared. • The application-oriented performance of the transformer-mixed model is evaluated. [ABSTRACT FROM AUTHOR]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is crucial for effectively regulating regional heat network systems. However, existing forecasting methods often rely on subjective experience to determine the forecasting step, which is limited by the presence of thermal inertia, leading to suboptimal accuracy. To address this limitation, an optimal step size selection method based on the Informer-based framework is proposed to enhance load forecasting accuracy in heat exchange stations. This method leverages the Attention mechanism within the Informer model, enabling the capture of global information in a single step. To verify the effectiveness of the proposed method, real operational data from a typical thermal power plant in North China is utilized to analyze and test the impact of data distribution and prediction step size on the model's prediction capability. The performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Comparative analysis against Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) models demonstrates that the Informer algorithm with optimal prediction step size achieves the highest prediction accuracy. Notably, the proposed method achieved a minimum reduction of 62.7 %, 46.5 %, and 42.9 % in MSE, MAE, and MAPE, respectively, significantly surpassing the performance of alternative prediction methods. • A method that can improve the accuracy of load forecasting in DHS is proposed. • The method can optimize the forecasting step size based on the Informer. • The effects of dataset, step size, and model chosen on the accuracy are investigated. • The Informer model with optimal prediction step enhanced the forecasting accuracy. [ABSTRACT FROM AUTHOR]