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Prediction of Chaotic Time Series Based on SALR Model with Its Application on Heating Load Prediction

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8171-8187页

作者： Zhang, Minghui Wang, Baozhu Zhou, Yatong Gu, Jihao Wu, Yuheng Hebei Univ Technol Sch Elect & Informat Engn 5340 Xiping Rd Tianjin 300401 Peoples R China

With the development of nonlinear science, improving the prediction performance of chaotic time series is of great significance in industrial production and daily life. Now, researchers have to develop effective models to achieve accurate prediction performance. The echo state network (ESN) has been proven to be an excellent prediction tool. However, the ESN has been criticized for not being principled enough. Thus, a novel ESN model namely self-join adjacent-feedback loop reservoir (SALR) is proposed. This model achieves the simplest topology structure on the premise of ensuring that all the connection modes of the classic ESN are available. In addition, in order to ensure the prediction performance of the network, the whale optimization algorithm was used to solve the parameter selection problems in the traditional cycle reservoir (SCR) model, the adjacent-feedback loop reservoir (ALR) model, and the SALR model. Finally, we use the proposed SALR model to solve classic benchmark chaotic time series as well as practical heating load prediction problems, and compare the SALR with the ESN, SCR, and ALR, respectively. Experimental results show that the proposed model can obtain higher accuracy with relatively low complexity than the ESN, SCR, and ALR.

关键词： Echo state network whale optimization algorithm Chaotic time series prediction Heating load prediction

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Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index

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APPLIED ENERGY 2023年第1期339卷

作者： Kumar, Abhishek Deng, Yan He, Xiangning Singh, Arvind R. Kumar, Praveen Bansal, R. C. Bettayeb, M. Ghenai, C. Naidoo, R. M. Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Indian Inst Technol IIT Guwahati Dept Elect & Elect Engn Gauhati India Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia Univ Sharjah Sustainable & Renewable Energy Engn Dept Sharjah U Arab Emirates

This research work presents the tri-level optimization framework for the optimal scheduling of grid-connected and autonomous microgrids to diminish power losses and maximize loadability. Since the network's voltage profile depends on the loading level, the flexible load shaping-based demand-side management strategy is incorporated to investigate its impact on microgrid loadability. With the consideration of uncertain parameters related to renewable power generation, load demand, and power loss, voltage limit constraints, the resultant problem is formulated as a stochastic mixed-integer non-linear problem to enhance microgrid loadability and optimize daily operating costs. The interdependency of demand side management program and microgrid loadability is investigated. The seasonal load profiles covering the weekend and weekday loads in winter, summer, and spring/fall seasons are examined in this research work. The enhanced versions of the distribution networks IEEE-33 and IEEE-69 based microgrid test systems are chosen to evaluate the proposed framework in both off-grid and autonomous modes of operation. Simultaneously, the overall customer satisfaction index is evaluated and improved according to the seasonal load profiles winter weekday, winter-weekend, summer -weekday, summer-weekend, spring-weekday, and spring-weekend by 8.68%, 7.97%, 16.7%, 19.62%, 17.14%, 20.50% respectively. The recently reported whale optimization algorithm is adopted to solve the proposed optimization problem, and the obtained simulation results are validated by comparing them with popular metaheuristic algorithms. The computational burden on the utility is reduced for optimal scheduling of grid -integrated microgrid to extract maximum power by maintaining network voltage profile.

关键词： Energy Management Loadability whale optimization algorithm Microgrid Operation and Planning

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Multikernel correntropy based robust least squares one-class support vector machine

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NEUROCOMPUTING 2023年第1期545卷

作者： Zheng, Yunfei Wang, Shiyuan Chen, Badong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Least-squares one-class support vector machine (LS-OCSVM) is one of the most popular methods to perform one-class classification tasks, in which only the data of a specific class are available to train the classification model. However, the learning performance of LS-OCSVM heavily relies on the effectiveness of a squared loss function, which is sensitive to outliers, resulting in the poor robustness of LS-OCSVM to deal with contaminated data. In this paper, the original optimization problem of LS-OCSVM is therefore reformulated with a recently proposed robust similarity measure, called multikernel correntropy, generating a multikernel correntropy based LS-OCSVM (MKCLS-OCSVM). To find the solution to the new optimization problem effectively, a dynamic optimization algorithm developed with the popular half-quadratic optimization technique is adopted to perform the optimization process. Meanwhile, the convergence and computational complexity of the developed optimization algorithm are analyzed from theoretical perspectives. To further facilitate the implementation of MKCLS-OCSVM, an operationally simple search strategy, inspired by the hunting behavior of humpback whales, is designed for parameters selection. Experimental results on various one-class classification tasks are reported to demonstrate the performance superiority of the proposed MKCLS-OCSVM in comparison with LS-OCSVM and other robust LSOCSVM variants.

关键词： Least -squares one -class support vector machine One -class classification Multikernel correntropy Half -quadratic optimization technique whale optimization algorithm

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A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism

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ENERGY 2023年第1期269卷

作者： Yu, Min Niu, Dongxiao Gao, Tian Wang, Keke Sun, Lijie Li, Mingyu Xu, Xiaomin North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China Zhengzhou Univ Sch Management Engn Zhengzhou 450001 Peoples R China

With resource shortages and global warming becoming increasingly serious, it is urgent to accelerate the tran-sition to green and low-carbon energy. Wind power, as a kind of green, low-carbon, zero-cost renewable energy, has undergone rapid development. Aiming to address the problem of strong randomness and strong temporal correlations in wind power prediction (WPP), a new framework for WPP based on RF-WOA-VMD and BiGRU optimized by an attention mechanism is proposed. Firstly, the random forest algorithm (RF) is adopted to screen the influencing factors of wind power, effectively reducing the data redundancy and improving the prediction efficiency. Secondly, the variational modal decomposition (VMD) algorithm optimized by the whale algorithm (WOA) for WPP is adopted, which uses the WOA to adaptively determine the optimal parameters [K, alpha] in VMD, adaptively decompose raw wind power series, and reduce data noise. Furthermore, the BiGRU algorithm opti-mized by the attention mechanism is proposed for WPP. The attention mechanism is introduced to assign different weights to the hidden states of BiGRU to emphasize the impact of key information. Ultimately, the experimental result illustrated that the proposed model further enhances the prediction accuracy. According to data set 1, MAPE is reduced by 86.81% compared with BiGRU.

关键词： Wind power Random forest Variational modal decomposition whale optimization algorithm Attention mechanism Bidirectional gated recurrent unit

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Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Ma, Shengshan Yuan, Jie Guo, Zhenyu Wu, Qiong Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

Since a plume's visual characteristics have not been fully considered and utilized, the traditional plume nearsource search methods lose a large amount of potentially available information, resulting in the low efficiency and reliability of near-source search. This paper proposes a novel plume near-source search approach assisted by the intermittent visible plume information using finite state machine and YOLOv3-tiny for a robot. A finite state machine is used to realize the autonomous behavioral decision-making of the robot according to the obtained visible plume information, olfactory information, and lidar information. In addition, the YOLOv3-tiny network is used to detect the intermittent visible plume information obtained by a depth camera to capture the color and texture of the plume. In order to improve the search reliability of the robot in the olfactory tracking behavior, a whale optimization algorithm is utilized to enable the robot to simulate the hunting behavior and the social mechanism of the whale group and conduct olfactory tracking of the plume. The experimental results show that this proposed plume near-source search approach achieves the success rates of 91.67% and 83.33% in windless and windy environments respectively, and the average total running time is 113.8 s and 159.6 s in the experimental environment with obstacles, respectively.

关键词： Intermittent Visible Plume Information YOLOv3-tiny Finite State Machine whale optimization algorithm Near-Source Search

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Capacity-based optimization using whale optimization technique of a power distribution network

ENGINEERING REPORTS

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ENGINEERING REPORTS 2022年第1期4卷

作者： Simachew, Bawoke Khan, Baseem Guerrero, Josep M. Padmanaban, Sanjeevikumar Mahela, Om Prakash Alhelou, Hassan Haes Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Hawassa Univ Dept Elect & Comp Engn Awasa Ethiopia Aalborg Univ Ctr Res Microgrids CROM Aalborg Denmark Aarhus Univ CTiF Global Capsule Dept Business Dev & Technol Herning Denmark Rajasthan Rajya Vidyut Prasaran Nigam Ltd Power Syst Planning Div Jaipur Rajasthan India Tishreen Univ Dept Elect Power Engn Latakia Syria

In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi-objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.

关键词： capacitor placement distributed generation loss minimization particle swarm optimization whale optimization algorithm

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A theoretical approach based on machine learning for estimation of physical properties of LLDPE in moulding process

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Zhong, Fan Xian Traff Engn Inst Sch Mech & Elect Engn Xian 710300 Shanxi Peoples R China

This study explores the prediction of mechanical characteristics of linear polyethylene based on oven residence time, employing various regression models and hyper-parameter tuning through the whale optimization algorithm. The dataset comprises one input variable (oven residence time) and three output parameters (Tensile Strength, Impact Strength, and Flexure Strength). The models investigated include Multilayer Perceptron, K-Nearest Neighbors, Support Vector Regression, Polynomial Regression, and Theil-Sen Regression. The results showcased distinct performances across the models for each output parameter. The Polynomial Regression (WOA-PR) method has been identified as the most suitable option for predicting Tensile Strength due to its ability to achieve the lowest errors in terms of Mean Absolute Error, Root Mean Square Error, and Average Absolute Relative Deviation. K-Nearest Neighbors (WOA-KNN) outperforms other models in predicting Impact Strength due to its superior accuracy and reliability. Additionally, Support Vector Regression (WOA-SVR) emerges as the best model for predicting Flexure Strength, showcasing notable performance in minimizing prediction errors. These findings underscore the significance of model selection and optimization techniques in accurately predicting the mechanical properties of polymers, paving the way for enhanced manufacturing processes and material design.

关键词： Polymer Regression models whale optimization algorithm Model optimization

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Optimal Dump Load Allocations in High RBDG Penetrated Microgrid for Voltage and Frequency Regulation

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第2期46卷 1511-1528页

作者： Uniyal, Ankit Sarangi, Saumendra Rawat, Mahiraj Singh Natl Inst Technol Uttarakhand Pauri Garhwal Uttarakhand India Motilal Nehru Natl Inst Technol Allahabad Prayagraj Uttar Pradesh India

The rising renewable penetrations has paved ways for the microgrids to operate independent of the conventional centrally located power plants. At low load hours, the microgrid operation is often disrupted due the excess power generations creating voltage (V) and frequency (f) regulation issues. The use of energy storage devices to absorb such excess generations would be infeasible due to their less usability, high cost and uneconomical trading. The offline planning of distributing dump loads (DLs) in the network can coordinate with the online control in maintaining V and f of the system. Thus, the present work provides an offline analysis to optimally allocate DLs in the power electronic (PE) interfaced DGs powered autonomous microgrid to minimize the V and f deviations at off-peak hours. The V and f deviations as per the droop characteristics of PE interfaced DGs have been taken into consideration in the load flow. The optimization problem is formulated and solved using heuristic techniques viz MWOA and NSGA-II. The weakly meshed standard test beds of IEEE 33 and 69 bus distribution systems have been used in the present work as autonomous microgrids.

关键词： Distributed generation Dump loads Load flow Microgrids Non-dominated sorted genetic algorithm whale optimization algorithm

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Advanced modeling based on machine learning for evaluation of drug nanoparticle preparation via green technology: Theoretical assessment of solubility variations

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CASE STUDIES IN THERMAL ENGINEERING 2023年 45卷

作者： Begum, M. Yasmin King Khalid Univ KKU Coll Pharm Dept Pharmaceut Abha 62529 Saudi Arabia

Variability of drug solubility in supercritical solvent was investigated theoretically in this study using multiple machine learning models. The method of supercritical preparation can be used to manufacture solid-dosage drugs with enhanced solubility which can increase drug bioavailability and efficacy. In this study, we applied bagging regression to three core models, namely SVM, KNN, and linear regression (LR) to predict the solubility and density of Hyoscine drug based on Temperature and Pressure parameters as inputs. The WOA optimization algorithm was used to tune hyperparameters, and the performance of these models was compared with r2, RMSE, MAE, and AARD% metrics. The results revealed that the BAG-KNN model outperformed the others in predicting solubility, with an r2 score of 0.989 and a low RMSE of 0.041. Also, the BAG-LR model had the highest AARD% value of 7.50974, indicating that it has higher average relative deviation from the actual density values. Overall, the bagging approach proved to be effective in improving the accuracy of the core models, and the WOA optimization algorithm was successful in finetuning their hyperparameters. These findings can have significant implications for the accurate prediction of chemical properties in various fields, such as drug discovery and material science.

关键词： Nanomedicine Modeling Pharmaceutical manufacture Drug efficacy Machine learning whale optimization algorithm

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Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method

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OCEAN ENGINEERING 2023年第Part2期288卷

作者： Leng, Jiaxuan Incecik, Atilla Wang, Mengmeng Feng, Shizhe Li, Yongbo Yang, Chunsheng Li, Zhixiong Ocean Univ China Dept Marine Technol Qingdao 266001 Peoples R China Univ Strathclyde Dept Naval Architecture Ocean & Marine Engn Glasgow G11XQ Scotland Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Guangzhou Univ Inst Artificial Intelligence Guangzhou 510006 Peoples R China Donghai Lab Zhoushan 316021 Peoples R China Opole Univ Technol Fac Mech Engn PL-45758 Opole Poland

The artificial intelligence (AI) technologies, such as meta-heuristic computing and deep learning, have provides solid technical support for structural health monitoring (SHM) of offshore jackets. In this paper, a physicsenhanced AI method based on the parametric damage identification is developed for SHM of the offshore jacket structures. In this new method, a hybrid kernel function-based kernel extreme learning machine (HKELM) is proposed to construct an AI structure to enhance the SHM detection capacity on the structural modal parameters extracted by the parametric damage identification technique. Simulation analysis is carried out to verify the feasibility of the HKELM-based method using the response signals of a jacket structure under impact force, and the result demonstrates good damage location capability of the proposed method. Furthermore, to select proper parameters for the HKELM, the whale optimization algorithm (WOA) is applied to optimize the values of the regularization coefficient and kernel parameter array. Then, the wavelet denoising (WD) is introduced to preprocess the vibration signals to improve the damage detection ability of the WOA-HKELM. Lastly, experimental tests are performed to validate the effectiveness of the proposed method in utilizing the structural modal parameters for identifying the structural damage. The analysis results illustrate that the proposed method produces satisfactory damage location ability under the influence of actual noise in the vibration signals. Meanwhile, this method has broad application prospects in the SHM of other offshore structures.

关键词： Damage location Hybrid kernel extreme learning machine whale optimization algorithm Offshore jacket structures

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