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Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and whale optimization

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ENERGY 2022年 260卷

作者： Deng, Yanqiao Ma, Xin Zhang, Peng Cai, Yubin Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Sch Econ & Management Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Ctr Informat Management & Serv Studies Sichuan Mianyang 621010 Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China

Coal-to-gas switching in urban areas plays an important role in accelerating the pace of carbon neutrality. Accurate urban gas load forecasting is beneficial in balancing the peak-valley difference and achieving high-efficiency gas utilization. This work aims to develop a new method based on Tanimoto kernel-based nonlinear autoregressive (NAR) model for dynamical modelling. The Tanimoto kernel is extended to be available for regression modelling for the first time, and of which some important properties are analyzed. Besides, a new optimization scheme based on holdout validation and whale optimization algorithm is introduced for hyperparameter optimization. Then, the proposed Tanimoto kernel-based NAR model is applied for 5-step ahead forecasting with four regular lags 6, 9, 12, and 24 of short-(2015/1/1-2015/12/31), medium-(2014/1/1-2015/12/31), and long-(2013/1/1-2015/12/31) term daily urban gas load (UGL) in Chengdu. Results show that the proposed Tanimoto kernel-based model always produces higher precision in 80% of sub-cases than the other 11 kernel models and 8 machine learning models, with the one-step ahead forecasting mean absolute percentage error (MAPE) ranging from 2.3375% to 3.8765%, less than the other models ranging from 0.2335% to 34.5432%, and the proposed optimization scheme is efficient in improving the model's generalization ability and robustness.

关键词： Daily urban gas load forecasting Least squares support vector regression The Tanimoto kernel Nonlinear autoregressive model whale optimization algorithm

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Noise reduction method of plastic pipe leakage signal based on WOA-VMD algorithm 9

Noise reduction method of plastic pipe leakage signal based ...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Chen, Mingyu Zhang, Xiaoxiao Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264026 Peoples R China

ISBN: (纸本)9781665464215

Aiming at the problem that it is difficult to select the appropriate number of decompositions K and penalty factor alpha for the denoising of plastic pipe leakage signal by variational modal decomposition (VMD) algorithm. In this paper, a noise reduction method based on whale optimization algorithm (WOA) combined with VMD algorithm was proposed. First, the permutation entropy can reflect the complexity of the one-dimensional time series, and the decomposition result with the smallest permutation entropy is taken as the optimization objective;then the permutation entropy is used as a fitness function combined with WOA to optimize the parameters of the VMD algorithm to realize the adaptive decomposition of the leakage signal;Finally, calculate the correlation coefficient of each intrinsic mode function (IMF) component after decomposition to judge its noise level, remove the high frequency component which contains much noise, so as to realize the noise reduction of pipeline leakage signal. The simulate experimental results show that the method has a good denoising effect on the leakage signal of plastic pipeline, and can effectively improve the signal-to-noise ratio (SNR) of the leakage signal, which proves its feasibility in the actual leakage detection of plastic pipeline.

关键词： variational modal decomposition whale optimization algorithm correlation coefficient leak detection

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A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method

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RENEWABLE ENERGY 2021年 179卷 2198-2211页

作者： Nezhad, M. Majidi Heydari, A. Pirshayan, E. Groppi, D. Garcia, D. Astiaso Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy

Wind energy produced in near and offshore farms is constantly increasing, mainly thanks to the use of new technologies and the economic costs decrease. Data coming from satellites recently launched can be effectively used in near and offshore areas for wind speed mapping, and long and short-term analyses of wind regimes. Furthermore, the use of artificial intelligence methods is constantly increasing for predicting wind energy production. In this framework, a new forecasting model for wind speed assessment is presented integrating Sentinel satellite imagery analysis, in two phases using multi-sensor satellites, and machine learning methods. In the first step, wind speed and bathymetry have been analysed by means of sentinel-1 (S-1) and sentinel-2 (S-2) satellites images, respectively. Furthermore, a hybrid forecasting model has been proposed to assess and predict wind speed. The machine learning model consists of an integrated model using generalized regression neural network (GRNN) and the whale optimization algorithm (WOA). The new developed method has been then applied to assess wind energy potential around the Favignana island in Sicily, Italy. The results show that all the important primary parameters for a wind farm installations potential analysis, such as wind speed, water depth, and distance to the shoreline can be successfully analysed in a possible short time and freely. The following results have been obtained testing the new method: i) S-1 and S-2 satellite images have good potential for near and offshore wind speed assessment and bathymetry detection around Favignana island, ii) the root mean square error (RMSE), mean square error (MAE), and mean absolute percentage error (MAPE) of the proposed forecasting model using the whole dataset are 0.0205, 0.0159, and 6.8385 respectively, iii) the proposed forecasting model for wind speed has higher accuracy than other valid models. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind speed assessment Relative water depth Sentinel satellites family Generalized regression neural network whale optimization algorithm

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Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

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EXPERT SYSTEMS 2023年第3期40卷 e13208-e13208页

作者： Ranasingh, Soumyaranjan Pradhan, Tapan Dhenuvakonda, Koteswara Raju Natl Inst Technol Dept Elect Engn Silchar India

Calibration is the backbone of any sensor and measurement philosophy. The conventional calibration techniques for electrical parameter measurement using nonlinear sensors are affected by the repeating analogue signals and lead to errors in measurement. This research paper investigates different regression-based mathematical models to calibrate the Hall sensor for measuring RMS and the fundamental frequency. The novelty of this research work lies in the feature-based input modelling to measure RMS current and frequency with an error of 3.37e-12% and 7.61e-9%, respectively. The conventional Fourier transform method is compared with six different bio-inspired metaheuristic algorithms to estimate the frequency components of the analogue signal received from the measurement setup. Apart from the conventional sine waveforms, this paper investigates sawtooth and square waveforms as the periodic signals for determining the frequency components. The results from the comparison study show that the whale optimization algorithm exhibits 1.25% lesser error whilst predicting the features in measured frequency components. Apart from this, the paper identifies a unique combination of features that effectively measures the instantaneous electrical parameter with RMSE, NRMSE, and R2 value of 158.8, 0.51, and 0.44, respectively. Experimentally it is found that the decision tree and random forest regression models calibrate the Hall sensor with 93% less error than their linear and polynomial counterparts.

关键词： calibration current sensor decision tree regression frequency estimation regression models whale optimization algorithm

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An efficient approach for optimum shape design of steel shear panel dampers under cyclic loading

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SMART STRUCTURES AND SYSTEMS 2021年第3期27卷 547-557页

作者： Khatibinia, Mohsen Ahrari, Aghdas Gharehbaghi, Sadjad Sarafrazi, Seyyed Reza Univ Birjand Dept Civil Engn Birjand Iran Sharif Univ Technol Dept Civil Engn Tehran Iran

The low-cycle fatigue performance of shear panel damper (SPD) highly depends on the geometry of its shape and the criterion considered for its design. The main contribution of the current study is to find the optimum shape of the SPD subjected to cyclic loading by considering two different objective functions. The maximum equivalent plastic strain and the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain are selected as the first and second objective functions, respectively. Since the optimization procedure requires high computational efforts, a hybrid computational approach is used to perform two paramount phases of estimating the inelastic responses of the SPD and solving the optimization problem. In the first phase, as an alternative for the time-consuming finite element analysis of the SPD, a weighted-support vector machine model is developed to predict the inelastic responses of the SPDs during the optimization process. In the second phase, the optimum shape of the SPD is found by using the whale optimization algorithm (WOA). The results indicate that both design criteria lead to the optimum-shaped SPDs with a significant improvement in their low cycle fatigue performance in comparing with the initial rectangular shape while a slight reduction in their energy dissipation capacity. Moreover, the second design criterion is slightly better in the performance improvement of the optimum-shaped SPDs compared with the first one. In addition, the weighted-based SVM approach can accurately predict the inelastic responses of the SPDs under cyclic loading, and its combination with WOA results in finding the optimum solutions quickly.

关键词： shear panel damper energy dissipation shape optimization support vector machine whale optimization algorithm

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Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

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JOURNAL OF SUPERCOMPUTING 2021年第10期77卷 11643-11681页

作者： Deng, Zexi Cao, Dunqian Shen, Hong Yan, Zihan Huang, Huimin Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Guangxi Univ Nationalities Sch Sci Nanning 530006 Guangxi Peoples R China

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.

关键词： Task scheduling whale optimization algorithm Opposition-based learning Heterogeneous multiprocessor systems

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Fault feature extraction of rolling element bearing based on EVMD

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第12期43卷 1-14页

作者： Zhu, Danchen Liu, Guoqiang He, Wei Yin, Bolong Naval Petty Officer Acad Bengbu 233012 Peoples R China Guangzhou Mech Engn Res Inst Co Ltd Equipment Condit Detect Inst Guangzhou 510700 Peoples R China

Aiming at the problem that the bearing fault signal is weak and usually interfered by the strong background noise, which makes the fault feature extraction very difficult, an enhanced variational mode decomposition (EVMD) technique is proposed. First, the autoregressive (AR) model was utilized to eliminate the stationary components in the signal in advance to reduce the noise interference and the maximum kurtosis of the residual signal was set as the target. Second, the maximum frequency-domain correlated kurtosis was adopted as the fitness value, and the decomposition modes K and quadratic penalty factor alpha in the VMD approach were adaptively selected by the whale optimization algorithm. Third, the reconstruction signal was acquired, then the enhanced envelope spectrum was employed to weaken the interference of irrelevant frequency components and the fault features of rolling element bearing could be extracted accurately. The results of simulation and experimental analysis show that the proposed algorithm can significantly reduce the noise interference and avoid the blindness selection of VMD parameters. The comparison with fix-parameter VMD and fast kurtogram approaches shows that the proposed technique can improve the effectiveness of defect signature extraction, which has a certain value for engineering application.

关键词： AR model whale optimization algorithm Variational mode decomposition Rolling element bearing Fault feature extraction

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Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年 1-17页

作者： Rafigh, Parisa Akbari, Ali Akbar Bidhandi, Hadi Mohammadi Kashan, Ali Husseinzadeh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.

关键词： Sustainable supply chain Stochastic programming Multi-objective optimization whale optimization algorithm COVID-19 pandemic

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Prediction-based Distributed State Estimation Control for IoT-Enabled Train Positioning Systems

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IEEE Access 2025年 13卷 101922-101933页

作者： Ponnarasi, L. Pankajavalli, P.B. Lim, Y. Sakthivel, R. Alfarhood, Sultan Safran, Mejdl Bharathiar University Department of Computer Science Coimbatore 641046 India Sungkyunkwan University Department of Mathematics Suwon 440746 South Korea Bharathiar University Department of Applied Mathematics Coimbatore 641046 India King Saud University College of Computer and Information Sciences Department of Computer Science P.O.Box 51178 Riyadh 11543 Saudi Arabia

This study addresses a prediction and distributed state estimation-based reliable control problem for IoT-enabled train positioning systems which are subject to deception attacks, missing measurements, coupling delay, process and measurement noises. To mitigate the adverse effects of sensor attacks and faults on estimation performance, a novel resilient distributed state estimation method is proposed. During abnormal measurements, a saturation mechanism with adaptive bounds is embedded into each local estimator to limit the impact of distorted measurements for preventing them from exceeding acceptable boundaries. The modified local estimates are then transmitted to the state estimation center to generate a fused estimate. Using Lyapunov stability theory and stochastic analysis, sufficient conditions are derived in the form of linear matrix inequalities to obtain estimator and control gains. Furthermore, the whale optimization algorithm combined with the distributed state estimation scheme is employed to optimally adjust the weighting values of individual state estimates, thereby reducing estimation error. As a result, the improved state estimation enhances the reliability and performance of the controller, contributing to more accurate and stable train operation. Finally, the simulation results demonstrate the efficiency of the proposed predictive estimator design under output measurements affected by diverse noises, anomalous reading, varying train operational states and external disturbances. These results confirm that the proposed approach is robust and reliable, making it suitable for practical implementation in high-speed train systems. © 2013 IEEE.

关键词： Cyber-attacks Internet of Things Physical faults Prediction-based distributed filtering Train positioning system whale optimization algorithm

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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products

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CASE STUDIES IN CONSTRUCTION MATERIALS 2025年 22卷

作者： Mohsennia, Ehsan Javid, Alireza Toufigh, Vahab Sharif Univ Technol Dept Civil Engn Tehran Iran

The incorporation of fly ash, ground granulated blast furnace slag (GGBFS), and other pozzolanic materials into cement formulations presents a sustainable solution for utilizing industrial waste. This study introduces a novel, dual-stage machine learning framework that uniquely integrates both physical mix parameters and detailed chemical compositions of cement and multiple industrial by-products (IBPs)-including fly ash, GGBFS, silica fume, and metakaolin-into a unified predictive model. This approach significantly advances current methodologies, which often rely on single IBPs or exclude oxide-level descriptors. The framework first predicts ultrasonic pulse velocity (UPV), then uses the predicted UPV as an input to enhance compressive strength (CS) prediction-an underexplored configuration in previous studies. This novel approach is aligned with environmental management principles, aiming to maximize the effective use of diverse IBPs, reduce waste, and enhance resource efficiency. A robust dataset, comprising 162 structured IBP concrete samples and 524 data points from existing literature, enabled rigorous training and validation of sophisticated ML models. Among the models tested, the CatBoost (CB) algorithm, optimized with the whale optimization algorithm (WOA), exhibited outstanding predictive performance. The CB-WOA model achieved coefficients of determination (R2) values of 0.96 and 0.94 for UPV during training and testing, and 0.99 and 0.98 for CS, respectively. These results underscore the potential of UPV as a non-destructive metric for assessing concrete structural integrity and predicting material strength. A comprehensive sensitivity analysis further highlighted the importance of targeted feature selection, enhancing prediction accuracy and contributing directly to sustainability goals by optimizing material usage and improving construction practices. To facilitate the practical application of these findings, a user-friendly web application has been dev

关键词： Industrial by-products Concrete sustainability Machine learning Ultrasonic pulse velocity Compressive strength whale optimization algorithm

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