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An error-corrected deep Autoformer model via bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction

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APPLIED ENERGY 2025年 377卷

作者： Chen, Jie Peng, Tian Qian, Shijie Ge, Yida Wang, Zheng Nazir, Muhammad Shahzad Zhang, Chu Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Accurate PV power prediction is crucial for stable grid operation and rational dispatch. However, due to the instability of PV power generation, PV power prediction still has great challenges. Therefore, an Autoformer model based on secondary decomposition, bayesian optimization and error correction for PV power prediction. In order to reduce the complexity of the data and fully extract the features, two decomposition methods are employed. First, empirical mode decomposition (EMD) is applied to decompose the PV power series at the first level. Then, the sample entropy (SE) is introduced to measure the complexity of each component, and the variational mode decomposition (VMD) is employed to implement secondary decomposition of the component with the highest complexity. Secondly, a bayesian optimization algorithm enhanced Autoformer model is developed for predicting each component, and the predicted component results are aggregated to obtain preliminary PV power prediction results. Finally, the preliminary prediction results are error corrected using a least squares support vector machine. A four-month PV dataset from a PV power plant in Hangzhou, China is utilized to validate the effectiveness of the proposed model. The experimental results show that the model after primary decomposition is superior to the single model, and the prediction accuracy is substantially improved after secondary decomposition. The proposed model has the best prediction performance in predicting the PV power for different seasons, which shows good robustness.

关键词： Photovoltaic power prediction Autoformer Secondary decomposition Error correction bayesian optimization algorithm

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An online adaptive ROP prediction model using GBDT and bayesian optimization algorithm in drilling

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2025年 246卷

作者： Hao, Jiasheng Xu, Haomin Peng, Zhinan Cao, Zhen Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

As global energy demand continues to grow, improving drilling efficiency and reducing costs become pivotal factors in the advancement of oil and gas industry. As a core component of drilling optimization, measuring the rate of penetration (ROP) is crucial for enhancing the efficiency and profitability of the extraction process. Employing machine learning techniques to predict ROP represents an effective strategy. However, accurately predicting ROP within the diverse and dynamic stratigraphic environments remains a critical challenge. This paper proposes a novel approach for adaptive drilling optimization based on online machine learning, aimed at improving adaptability to varying stratigraphic conditions. This approach consists of two components: a learning model using Gradient Boosting Decision Trees (GBDT) and an optimal drilling parameters selector based on bayesian optimization. Specifically, GBDT is adopted to construct the ROP prediction model, which is continuously updated by integrating newly acquired information while drilling and removing outdated data. The parameters selector identifies the optimal combination of drilling parameters through the online bayesian algorithm, thereby facilitating ROP predictions. Experimental data from multiple horizontal wells in southwestern China demonstrate that this method significantly enhances the performance of the ROP prediction model by 10%-20%, with each optimization process requiring only approximately 2 s. This improvement is consistent across various formation depths and regions.

关键词： bayesian optimization algorithm Gradient boosting decision tree (GBDT) Rate of penetration Online update

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bayesian optimization algorithm Based on Incremental Model Building 7th

Bayesian Optimization Algorithm Based on Incremental Model B...

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Yao, Jintao Kong, Yuyan Yang, Lei South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Guangzhou Coll Commerce Guangzhou 511363 Guangdong Peoples R China

ISBN: (纸本)9789811003561;9789811003554

In bayesian optimization algorithm (BOA), to accurately build the best bayesian network with respect to most metrics is NP-complete. This paper proposes an improved BOA based on incremental model building, which learns bayesian network structure using PBIL instead of greedy algorithm in BOA. The PBIL is effective to learn better bayesian network. The simulation results also show that the improved BOA has the better performance than BOA.

关键词： bayesian optimization algorithm PBIL Model building Incremental learning

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Exploiting Bivariate Dependencies to Speedup Structure Learning in bayesian optimization algorithm

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Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin Nikanjam Adel Rahmani School of Computer Engineering Iran University of Science and Technology

bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation bayesian optimization algorithm bayesian network model building bivariate interaction

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Task scheduling using bayesian optimization algorithm for heterogeneous computing environments

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APPLIED SOFT COMPUTING 2011年第4期11卷 3297-3310页

作者： Yang, Jiadong Xu, Hua Pan, Li Jia, Peifa Long, Fei Jie, Ming Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China SIEMENS Ltd Corp Technol Emerging Automat Beijing 100102 Peoples R China

Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on the bayesian optimization algorithm (BOA) for heterogeneous computing environments. In the proposed algorithm, scheduling is divided into two phases. First, according to the task graph of multiprocessor scheduling problems, bayesian networks are initialized and learned to capture the dependencies between different tasks. And the promising solutions assigning tasks to different processors are generated by sampling the bayesian network. Second, the execution sequence of tasks on the same processor is set by the heuristic-based priority used in the list scheduling approach. The proposed algorithm is evaluated and compared with the related approaches by means of the empirical studies on random task graphs and benchmark applications. The experimental results show that the proposed algorithm is able to deliver more efficient schedules. Further experiments indicate that the proposed algorithm maintains almost the same performance with different parameter settings. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Multiprocessor scheduling Heterogeneous Parallel computing bayesian optimization algorithm

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Biasing the transition of bayesian optimization algorithm between Markov chain states in dynamic environments

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INFORMATION SCIENCES 2016年 334卷 44-64页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Ahnh, Chang Wook Univ Isfahan Fac Comp Engn Hezar Jerib St Esfahan *** Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

When memory-based evolutionary algorithms are applied in dynamic environments, the certainly use of uncertain prior knowledge for future environments may mislead the evolutionary algorithms. To address this problem, this paper presents a new, memory-based evolutionary approach for applying the bayesian optimization algorithm (BOA) in dynamic environments. Our proposed method, unlike existing memory-based methods, uses the knowledge of former environments probabilistically in future environments. For this purpose, the run of BOA is modeled as the movements in a Markov chain, in which the states become the bayesian networks that are learned in every generation. When the environment changes, a stationary distribution of the Markov chain is defined on the basis of the retrieved prior knowledge. Then, the transition probabilities of BOA in the Markov chain are modified (biased) to comply with the defined stationary distribution. To this end, we employ the Metropolis algorithm and modify the K2 algorithm for learning the bayesian network in BOA in order to reflect the obtained transition probabilities. Experimental results show that the proposed method achieves improved performance compared to conventional methods, especially in random environments. (C) 2015 Elsevier Inc. All rights reserved.

关键词： bayesian optimization algorithm Dynamic environments Markov chain Memory-based method

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Using bayesian optimization algorithm for model-based integration testing

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SOFT COMPUTING 2022年第7期26卷 3503-3525页

作者： Rafe, Vahid Mohammady, Somayeh Cuevas, Erik Arak Univ Fac Engn Dept Comp Engn Arak *** Iran Univ Guadalajara Dept Elect CUCEI Av Revoluc 1500 Guadalajara Jalisco Mexico

Model-based testing is an automated process in which executable tests are derived from behavioral models of a system. Model checking is a verification technique to reveal errors in which all reachable states of a system can be generated as state space. In the literature, different approaches suggest using model checkers for model-based testing. Model checker explores all possible system states, so utilizing the various paths in the state-space as test cases seems a promising solution. However, these approaches suffer from two main challenges. The first challenge is state space explosion, which prevents generating all reachable states by the model checker. The second one is generating redundant test cases. Recently, several methods using meta-heuristic and evolutionary approaches have been proposed to cope with these problems. Therefore, exploring a portion of state space using an optimization approach to detect the test objectives can be a proper way to manage the state space explosion and generate an optimal test suite with the least redundancy. In this paper, a method is proposed using a bayesian optimization algorithm (BOA), and a model checker is as a bed to generate test cases for the service-oriented systems. In the proposed approach, the test suite is a set of paths on the state space starting from an initial state and leading to the state in which all the test objectives are satisfied. In this research, we have implemented BOA with three different structures in GROOVE toolset, an open-source toolset for designing and model checking graph transformation. Experimental results show that our solution generates better results in terms of coverage and speed in different case studies than the existing approaches.

关键词： Model-based testing Graph transformation bayesian optimization algorithm Integration testing Data flow

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A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With bayesian optimization algorithm

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IEEE ACCESS 2021年 9卷 1219-1232页

作者： Shang, Qiang Feng, Linlin Gao, Song Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Shandong Univ Technol Sch Marxism Zibo 255000 Peoples R China

Automatic Incident Detection (AID) is an important part of Intelligent Transportation Systems (ITS). A hybrid AID method using Random Forest-Recursive Feature Elimination (RF-RFE) algorithm and Long-Short Term Memory (LSTM) network optimized by bayesian optimization algorithm (BOA) is proposed in this article. Firstly, a relatively comprehensive set of initial variables is constructed using basic traffic variables and their combinations. Secondly, feature variables are selected from the initial variables using the RF-RFE algorithm. Then, the feature variables are used for training the LSTM network, and the hyper-parameters of the LSTM network are optimized by BOA. In addition, Synthetic Minority Over-Sampling Technique (SMOTE) is employed to solve the problem of imbalance between incident sample size and non-incident sample size. Finally, experiments are conducted using real-world data to test performance of the proposed method and compare with several state-of-the-art AID methods on multiple evaluation criteria. The experimental results illustrate that the proposed method achieved superior performance with respect to almost all the evaluation criteria. It also shows that the proposed method is promising for dealing with the problems of imbalance and small sample size of traffic incident data.

关键词： Detectors Prediction algorithms Volume measurement Support vector machines Roads Machine learning algorithms Feature extraction Traffic incident detection feature selection long short-term memory network bayesian optimization algorithm

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On Hyperparameter optimization of Machine Learning Methods Using a bayesian optimization algorithm to Predict Work Travel Mode Choice

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IEEE ACCESS 2023年 11卷 19762-19774页

作者： Aghaabbasi, Mahdi Ali, Mujahid Jasinski, Michal Leonowicz, Zbigniew Novak, Tomas Chulalongkorn Univ Transportat Inst Bangkok 10330 Thailand Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist PL-40019 Katowice Poland Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic

Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the bayesian optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.

关键词： optimization Support vector machines Transportation Machine learning Predictive models Bayes methods Decision trees bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice

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Deadlock detection in complex software systems specified through graph transformation using bayesian optimization algorithm

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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 131卷 181-200页

作者： Pira, Einollah Rafe, Vahid Nikanjam, Amin Arak Univ Dept Comp Engn Fac Engn Arak *** Iran KN Toosi Univ Technol Fac Comp Engn Tehran *** Iran

While developing concurrent systems, one of the important properties to be checked is deadlock freedom. Model checking is an accurate technique to detect errors, such as deadlocks. However, the problem of model checking in complex software systems is state space explosion in which all reachable states cannot be generated due to exponential memory usage. When a state space is too large to be explored exhaustively, using meta-heuristic and evolutionary approaches seems a proper solution to address this problem. Recently, a few methods using genetic algorithm, particle swarm optimization and similar approaches have been proposed to handle this problem. Even though the results of recent approaches are promising, the accuracy and convergence speed may still be a problem. In this paper, a novel method is proposed using bayesian optimization algorithm (BOA) to detect deadlocks in systems specified formally through graph transformations. BOA is an Estimation of Distribution algorithm in which a bayesian network (as a probabilistic model) is learned from the population and then sampled to generate new solutions. Three different structures are considered for the bayesian network to investigate deadlocks in the benchmark problems. To evaluate the efficiency of the proposed approach, it is implemented in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the proposed approach is faster and more accurate than existing algorithms in discovering deadlock states in the most of case studies with large state spaces. (C) 2017 Elsevier Inc. All rights reserved.

关键词： bayesian network bayesian optimization algorithm Deadlock detection Graph transformation system State space explosion

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