Adaptive Prediction Method Research Articles

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

An Internet of Things Task Scheduling Framework Based on Agile Virtual Network on Demand Service Model

In order to improve the efficiency of cloud computing resource utilization and avoid the problem of computing resource allocation and scheduling lagging behind load changes, the author proposes a cloud computing resource on-demand allocation and elastic scheduling method based on network load prediction. Firstly, the author takes the network load data of Wikimedia as the research object and proposes an adaptive two-stage multi network model load prediction method based on LSTM, i.e. ATSMNN-LSTM load prediction method). This method can classify the network load data into climbing and descending types based on the trend and characteristics of the input network load data, And adaptively schedule the input network load data to the LSTM load prediction model that matches its type for prediction based on the classification results. The author proposes a maximum cloud service revenue computing resource quantity search algorithm based on network load prediction (i.e. MaxCSPRNWP algorithm), which aims to improve cloud service revenue as the optimization objective. Under the premise of ensuring task service quality and system stability, the algorithm allocates cloud computing resources on demand and flexibly schedules them in advance based on the predicted network load results. The experimental results show that the ATSMNNLSTM load prediction method proposed by the author can obtain more accurate network load prediction results compared to other load prediction methods, and the MaxCSPR-NWP algorithm, which is based on network load prediction and is capable of effectively converting the network load prediction results into the required number of cloud servers, is the maximum cloud service revenue computing resource quantity search algorithm proposed by the author, not only does it achieve the early allocation and scheduling of cloud computing resources, thereby avoiding the impact of lagging behind in computing resource allocation and scheduling due to load changes on the quality of cloud computing task services and resource utilization efficiency, at the same time, it has also achieved on-demand allocation and flexible scheduling of cloud computing resources with the goal of improving cloud service revenue.

A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle

A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle

Research on an adaptive prediction method for restaurant air quality based on occupancy detection

This study evaluates the impact of personnel on air quality in air-conditioned restaurant environments during winter, summer, and transitional seasons, characterized by the peculiarities of personnel movement, high concentration of personnel in a short period of time, poor ventilation and high personnel density, leading to the accumulation of pollutants that can severely damage personnel health. This study examines carbon dioxide (CO2) and particulate matter (PM) concentrations and proposes an online adaptive model for their prediction.Environmental influence factors are analyzed through correlations of measured data, and appropriate input parameters (time-lagged cumulative number of people) are selected. The Auto-Regressive with Exogenous Inputs (ARX) model is used to predict the change of air quality with the movement and number of people, compared with classical models (LR, DT, SVM, ENS, GPR, NN, VARX). Results show that the ARX model performs excellently in predicting restaurant environments; the effect of different environmental factors on the predictive effectiveness of the model was also explored. When combined with real-time sensor data and AIC-based adaptive optimization, the prediction accuracy is further improved by more than 25 %. The R2 of the ARX model is 0.9549, the MAE is 0.8352 μg/m3 and the MAPE is 3.17 %. The adaptively optimized model can be adapted to different environments, overcoming the problem of poor adaptability of such models. Our results enable effective environmental control strategies in public spaces, contributing to Sustainable Development Goal (SDG) 3: Good Health and Well-being by reducing health risks associated with indoor air pollution.

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Multi-environment robust polarization navigation sensor and the parameter adaptive prediction method

As a type of photosensitive sensor, the polarization navigation sensor is susceptible to variations in external optical information. Experiments have demonstrated that light intensity is one of the factors influencing solution parameters. Considering that traditional polarization navigation sensor cannot detect light intensity effectively, a polarization sensor with light intensity detection capability is designed. Additionally, a neural network-based adaptive parameter method is proposed. By utilizing a neural network for the real-time solution degree of linear polarization (DLOP) and combining the light intensity information with polarization information from the new sensor, the method finally realizes the prediction of external solution parameters, thus enabling the solution model to adapt to the environmental variations. Finally, the effectiveness of the adaptive prediction method is verified by experiments. The results of outdoor experiments show that the adaptive prediction method reduces the mean square error (MSE) of angle of polarization (AOP) by approximately 42.11%, 44.78%, and 52.83% compared to the convention solution method under sunny, cloudy, and overcast conditions.

A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins

Background: Computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods have treated the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pocket residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. Methods: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. Results: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. Conclusion: Our analysis confirms that the screening performance of the various numbers of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.

Research on adaptive feature optimization and drilling rate prediction based on real-time data

In the drilling process, the rate of penetration (ROP) is an essential indicator of drilling efficiency. Accurate prediction of the rate of penetration (ROP) is crucial for improving drilling efficiency and minimizing costs. Conventional ROP predictions are typically made post-drilling, often resulting in low accuracy and limited applicability. To predict ROP more efficiently and accurately, an adaptive feature-optimized ROP prediction method based on real-time data was proposed. Initially, a fusion feature selection algorithm based on historical drilling data was designed and optimized using the Particle Swarm Optimization algorithm (PSO). On this basis, adaptive feature optimization was performed on real-time drilling datasets, and four different machine learning ROP prediction models were established using the selected feature parameters. The result shows that compared with the non-adaptive methods, the accuracy of the ROP prediction model using adaptive feature optimization is improved. The improvements are 27%, 44%, 47% and 47%, respectively, it demonstrates the significant advantages of using the designed adaptive feature optimization.

Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC

Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

Discrete-Time Visual Servoing Control with Adaptive Image Feature Prediction Based on Manipulator Dynamics.

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot's velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.

Online parameter identification based predictive pressure control for train electro-pneumatic braking systems with thermal effect

The electro-pneumatic braking system with ON/OFF solenoid valves has been widely used in trains due to its advantages and superiority. The undesirable impact of the thermal effect on the electro-pneumatic braking system leads to frequent valve switching, degradation of the pressure tracking performance and sometimes instability. This article presents an adaptive model predictive control approach to solve the pressure control problem under temperature uncertainty based on a switched unscented Kalman filter. First, a nonlinear switched dynamical model with the uncertain temperature parameter is derived for the electro-pneumatic braking system by comprehensively integrating its nonlinear, discontinuous dynamics and thermal effect. Using a switched unscented Kalman filter on the presented model of the system, the temperature parameter is accurately estimated to improve the model’s accuracy. Based on the corrected system model and the designed adaptive model predictive control method, the pressure tracking performance and the valves’ switchings of the electro-pneumatic braking system are improved, and the stability is guaranteed. The simulations and the experiments conducted for a braking system prototype confirm the performance validity of the proposed method.

Data-driven adaptive predictive frequency control for power systems with unknown and time-varying inertia

This paper proposes a novel data-based adaptive predictive frequency control method for multi-area power systems with unknown and time-varying inertia. Firstly, a data-based representation is built and updated at each instant based on behavioural system theory by using historical input–output data, where a moving horizon estimation method is used to deal with the unknown time-varying inertia issue adaptively. Then, the optimal frequency control signal is computed by solving an optimization problem under the framework of data-based predictive control. Simulation results on a power system with three control areas demonstrate the effectiveness of the proposed method.

A Vehicle Velocity Prediction Method with Kinematic Segment Recognition

Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.

VSG Frequency Regulation Strategy for Adaptive Model Predictive Control

When the active power of a Virtual Synchronous Generator (VSG) is unbalanced, the frequency will fluctuate significantly. Based on this, this article proposes an adaptive model predictive control method. By introducing a hyperbolic tangent function to construct an adaptive moment of inertia that includes frequency change and frequency change rate to provide the inertia for the island microgrid system, and the predicted model is updated in real-time; At the same time, model predictive control is used to calculate the optimal power reference value of VSG, strengthening the power support of VSG. The results indicate that under joint control improves the frequency regulation capability of VSG units and further enhances the system frequency performance, and the frequency deviation reduces from 0.15Hz to 0.05Hz, and the frequency change rate reduces from 0.58Hz/s to 0.5Hz/s. It is proved that the proposed control strategy can effectively suppress the maximum frequency offset and maximum frequency change rate during power fluctuations.

Adaptive Sound Velocity Profile Prediction Method Based on Deep Reinforcement Learning

Adaptive Sound Velocity Profile Prediction Method Based on Deep Reinforcement Learning

Adaptive Predictive Deadbeat Current Control of Single-Phase Multi-Tuned Shunt Hybrid Active Power Filters

This paper suggests an adaptive predictive deadbeat current control method for single-phase multi-tuned shunt hybrid active power filters (HAPFs) to improve the power quality of single-phase and three-phase four-wire utility grids. The HAPF structure eliminates the resonance between the passive power filter and the grid impedance. Furthermore, it can be integrated into passive filters to enhance their filtering performance. In this paper, a digital algorithm is proposed for managing the performance of each converter leg accurately. To do so, an exact model of the high-order system is developed, and the transfer function of the plant is calculated in continuous and discrete time domains. Then, a predictive deadbeat technique for HAPF current control is presented, which benefits from high accuracy, fast dynamics, and low sensitivity to system parameter mismatches. Extensive simulation and experimental tests are conducted and the results match well to confirm the success and appropriate performance of the overall system. Also, performance comparison with conventional solutions demonstrates the superiority of the suggested filtering technique.

Optimal Output Tracking for Switched Systems Under DoS Attacks: A Model-Free Adaptive Predictive Control Method

Optimal Output Tracking for Switched Systems Under DoS Attacks: A Model-Free Adaptive Predictive Control Method

TP-FRL: An Efficient and Adaptive Trajectory Prediction Method Based on the Rule and Learning-Based Frameworks Fusion

TP-FRL: An Efficient and Adaptive Trajectory Prediction Method Based on the Rule and Learning-Based Frameworks Fusion