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Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

SENSORS 2022年第24期22卷 9630页

作者： Guo, Xianchao Mo, Yuchang Yan, Ke Huaqiao Univ Fujian Prov Univ Key Lab Computat Sci Quanzhou 362021 Peoples R China Natl Univ Singapore Coll Design & Engn Dept Built Environm Singapore 117566 Singapore

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.

关键词： bayesian optimization algorithm bidirectional long short-term memory deep learning singular spectrum analysis short-term photovoltaic power forecasting

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A bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging

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OPTICS AND LASERS IN ENGINEERING 2022年 156卷

作者： Hu, Feng Zhou, Mengran Yan, Pengcheng Liang, Zhe Li, Mei Anhui Univ Sci & Technol Sch Elect & Informat Engn 168 Taifeng Rd Huainan 232001 Anhui Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Anhui Peoples R China

The precise classification of coal and gangue is a crucial link for effective sorting and efficient utilization. However, there are some shortcomings in traditional methods, such as water consumption, coal slime pollution, and great influence of environmental factors, and so on. Here, multispectral imaging technology combined with the convolutional neural network (CNN) was applied to classify coal and gangue, in which the hyperparameters of the CNN model were optimized by bayesian algorithm. The multispectral images in the range of 675-975 nm of 209 pieces of coal and 201 pieces of gangue, which came from the Huainan mining area, were collected. The CNN and traditional modeling methods (combination strategy of image feature extraction and classifier) were employed to develop identification models, and the classification results were analyzed and compared on the multispectral dataset of coal and gangue. The identification analysis model based on CNN had the best performance, and the F1 score reached 1.00. At this time, the hyperparameters of the model are as follows: network depth was 1, initial learning rate was 0.012939, random gradient descent momentum was 0.83813, and L2 regularization intensity was 0.0099852. Moreover, the robustness of the CNN identification model was verified by introducing different levels of noise signals. The identification analysis model based on the CNN can quickly and accurately identify coal and gangue without complex image processing steps, and the model has certain anti-interference ability, which will promote the progress of automatic separation technology for coal and gangue.

关键词： Multispectral imaging Convolutional neural network Coal-gangue identification bayesian optimization algorithm

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Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第5期33卷

作者： Qiu, Kepeng Song, Weihong Wang, Peng Tsinghua Univ Dept Precis Instrument State Key Lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China Heilongjiang North Tool Co Ltd Mudanjiang 157000 Heilongjiang Peoples R China

Abnormal data detection for industrial processes is essential in industrial process monitoring and is an important technology to ensure production safety. However, for most industrial processes, it is a challenge to establish an effective abnormal data detection model due to the following issues: (a) weak model performance due to the small amount of process data;(b) trade-offs between model sparsity and accuracy;and (c) weak generalization ability of abnormal data detection model. To address these issues, a method based on adversarial autoencoders support vector data description (AAESVDD) is presented in this work. First, a novel construction strategy is designed for a hybrid feature dataset based on the adversarial autoencoder (AAE). The hybrid feature dataset utilizes the latent feature and reconstruction residual extracted by the AAE to enhance the feature diversity of the process data. Then, combining the support vector data description (SVDD) and bayesian optimization algorithm (BOA), an automatic detection model for abnormal data of the hybrid feature dataset is established. Meanwhile, a BOA objective function based on the criterion of the hybrid risk minimization is proposed to automatically optimize the model parameters, which further enhances the generalization ability of the SVDD-based model. Finally, the effectiveness of the proposed AAESVDD method is illustrated with the UCI benchmark datasets and an industrial penicillin fermentation process.

关键词： industrial processes abnormal detection support vector data description adversarial autoencoders bayesian optimization algorithm

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AN ENTROPY APPROACH TO EVALUATION RELAXATION FOR bayesian optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第9期8卷 6371-6388页

作者： Hai Thi Thanh Nguyen Hoang Ngoc Luong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn Suwon 440746 South Korea Ctr Wiskunde & Informat NL-1090 GB Amsterdam Netherlands

bayesian optimization algorithm (BOA), a multivariate estimation of distribution algorithm, needs incorporating with efficiency enhancement techniques to be capable of solving difficult large-scale problems in a reliable and scalable manner. In this paper, we present a novel evaluation relaxation method which is based on the conditional entropy measurement. The concept of conditional entropy is rigorously analyzed and then is used to investigate the stability of the population. Especially, we utilize the evaluation relaxation strategy (ERS) proposed herein to determine whether a candidate solution should be evaluated by actual functions or be estimated by surrogate models. BOA coupled with our entropy-based ERS, termed en-BOA, shows its superiority in significantly reducing the total number of expensive fitness evaluations until reliable convergence. Experimental results prove that the entropy-based ERS enhances the efficiency of BOA while not negatively affecting the scalability of the original algorithm. In addition, the effects of our efficiency enhancement technique on population sizing requirements are also discussed.

关键词： bayesian optimization algorithm Conditional entropy Efficiency enhancement Evaluation relaxation Fitness evaluation

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MOBOpt - multi-objective bayesian optimization

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SOFTWAREX 2020年 12卷

作者： Galuzio, Paulo Paneque de Vasconcelos Segundo, Emerson Hochsteiner Coelho, Leandro dos Santos Mariani, Viviana Cocco Posit Univ Prof Pedro Viriato Parigot Souza 5300 BR-81280330 Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Imaculada Conceicao 1155 BR-80215901 Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn Cel Francisco Heraclito dos Santos 100 BR-81531980 Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Imaculada Conceicao 1155 BR-80215901 Curitiba Parana Brazil

This work presents a new software, programmed as a Python class, that implements a multiobjective bayesian optimization algorithm. The proposed method is able to calculate the Pareto front approximation of optimization problems with fewer objective functions evaluations than other methods, which makes it appropriate for costly objectives. The software was extensively tested on benchmark functions for optimization, and it was able to obtain Pareto Function approximations for the benchmarks with as many as 20 objective function evaluations, those results were obtained for problems with different dimensionalities and constraints. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： optimization problems Multi-objective optimization bayesian optimization algorithm

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Improving case-based reasoning in solving optimization problems using bayesian optimization algorithm

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INTELLIGENT DATA ANALYSIS 2012年第2期16卷 199-210页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Univ Isfahan Dept Comp Engn Esfahan 817467344 Iran

The Case-Based Reasoning (CBR) solves problems by using the past problem solving experiences. How to apply these experiences depends on the type of the problem. The method presented in this paper tries to overcome this difficulty in CBR for optimization problems, using bayesian optimization algorithm (BOA). BOA evolves a population of candidate solutions through constructing bayesian networks and sampling them. After solving the problems through BOA, bayesian networks describing solutions features are obtained. In our method, these bayesian networks are stored in a case-base. For solving a new problem, the bayesian networks of those problems which are similar to the new problem, are retrieved and combined. This compound bayesian network is used for generating the initial population and constructing the probabilistic models of BOA in solving the new problem. Our method improves CBR in two ways: first, in our method, how to use the knowledge stored in the case-base is disregarding the problem itself and is universally;second, this method stores the probabilistic descriptions of the previous solutions in order to make the stored knowledge more flexible. Experimental results showed that in addition to the mentioned advantages, our method improved the solutions quality.

关键词： Otimization case-based reasoning bayesian networks bayesian optimization algorithm

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Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 35卷

作者： Gazioglu, Emrullah Etaner-Uyar, A. Sima Istanbul Tech Univ Comp & Informat Dept Istanbul Turkey Fatih Sultan Mehmet Vakif Univ Comp Engn Dept Istanbul Turkey

Dynamic environments are still a big challenge for optimization algorithms. In this paper, a Genetic algorithm using both Multiploid representation and the bayesian Decision method is proposed. By Multiploid representation, an implicit memory scheme is introduced to transfer useful information to the next generations. In this representation, there are more than one genotypes and only one phenotype. The phenotype values are determined based on the corresponding genotypes values. To determine phe-notype values, the well-known bayesian optimization algorithm (BOA) has been injected into our algo-rithm to create a Bayes Network by using the previous population to exploit interactions between variables. With this algorithm, we have solved the well-known Dynamic Knapsack Problem (DKP) with 100, 250, and 500 items. Also, we have compared our algorithm with the most recent algorithm in the literature by using the DKP with 100 items. Experiments have shown that the proposed algorithm is effi-cient and faster than the peer algorithms in the manner of tracking moving optima without using an explicit memory scheme. In conclusion, using relationships between variables within the optimization algorithms is useful when concerning dynamic environments.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： optimization genetic algorithm evolutionary computation dynamic environments estimation of distribution algorithms bayesian optimization algorithm

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bayesian Immigrant Diploid Genetic algorithm for Dynamic Environments 1

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14th Biennial International Conference on Artificial Evolution (EA)

作者： Gazioglu, Emrullah Etaner-Uyar, A. Sima Istanbul Tech Univ Istanbul Turkey

ISBN: (数字)9783030457150

ISBN: (纸本)9783030457150

In dynamic environments, the main aim of an optimization algorithm is to track the changes and to adapt the search process. In this paper, we propose an approach called the bayesian Immigrant Diploid Genetic algorithm (BIDGA). BIDGA uses implicit memory in the form of diploid chromosomes, combined with the bayesian optimization algorithm (BOA), which is a form of Estimation of Distribution algorithms (EDAs). Through the use of BOA, BIDGA is able to take into account epistasis in the form of binary relationships between the variables. Experiments show that the proposed approach is efficient and also indicates that exploiting interactions between variables is important to adapt to the newly formed environments.

关键词： Evolutionary algorithms Estimation of Distribution algorithms bayesian optimization algorithm Dynamic environments

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Modeling and Sectional Compensation of Temperature Error of FOG Based on BOA-GBDT

Modeling and Sectional Compensation of Temperature Error of ...

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第33届中国控制与决策会议

作者： Cao Feng Yan Ting-yang Su Min School of Mechanical and Electrical Engineering Qilu Institute of Technology

According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesian algorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope,and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model *** fiber optic gyroscope is placed in a temperature box to perform a temperature change test of -40-60℃ to obtain measured *** temperature and temperature change rate are used as input,and bayesian algorithm optimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed *** comparative experiment results show that the proposed model achieves the best compensation *** the compensation comparison test,it is verified that the proposed model has good compensation ability and generalization ability for non-training data.

关键词： Fiber optic gyroscope Temperature error bayesian optimization algorithm Gradient lifting tree Sectional compensation

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A novel approach to solve AI planning problems in graph transformations

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 92卷 103684-103684页

作者： Pira, Einollah Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** Iran

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. bayesian optimization algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution algorithm (EDA) in which bayesian networks are used to evolve the solution populations. Actually, a bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.

关键词： bayesian optimization algorithm AI planning Graph transformation system bayesian network Refinement

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