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Short-Term Forecasting and Uncertainty Analysis of Wind Power

JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2021年第5期143卷 054503-054503页

作者： Bo, Gu Keke, Luo Hongtao, Zhang Jinhua, Zhang Hui, Huang North China Univ Water Resources & Elect Power Zhengzhou 450045 Peoples R China

Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

关键词： whale optimization algorithm least squares support vector machine model short-term wind power forecasting nonparametric kernel density estimation confidence interval probability density distribution renewable wind turbine

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Two hybrid meta-heuristic algorithms for a dual-channel closed-loop supply chain network design problem in the tire industry under uncertainty

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ADVANCED ENGINEERING INFORMATICS 2021年 50卷

作者： Fathollahi-Fard, Amir M. Dulebenets, Maxim A. Hajiaghaei-Keshteli, Mostafa Tavakkoli-Moghaddam, Reza Safaeian, Mojgan Mirzahosseinian, Hassan Univ Quebec Dept Elect Engn Ecole Technol Super 1100 Notre Dame St W Montreal PQ Canada Florida A&M Univ Florida State Univ FAMU FSU Coll Engn Dept Civil & Environm Engn 2525 Pottsdamer StBldg ASuite A124 Tallahassee FL 32310 USA Tecnol Monterrey Escuela Ingn & Ciencias Dept Ingn Ind Campus Puebla Monterrey Mexico Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran RMIT Univ Coll Business Sch Business & IT Logist Melbourne Vic 3000 Australia

There is so much interest in online purchasing within supply chain networks nowadays. After expanding the internet access and services, customers' behavior has changed. Today, a customer's shopping manner usually begins with the internet search. With this approach, we face some new trends in this field, such as online-to-offline (O2O) commerce that aims to balance online and offline sales. Regarding the supply chain management, the O2O commerce can help the managers to conduct both online and offline businesses. The tire industry is one of the applications of the O2O approach, which also directly affects the supply chain network design (SCND). Therefore, this work for the first time proposes a dual-channel, multi-product, multi-period, multi-echelon closed-loop SCND under uncertainty for the tire industry. To tackle the uncertain parameters of the problem (e.g., prices and demand), a fuzzy approach, so-called the Jimenez's method, is applied. Another main innovation of this work is two new hybrid meta-heuristic algorithms with new procedures. Two recent nature-inspired algorithms (i.e., red deer algorithm (RDA) and whale optimization algorithm (WOA)) are hybridized with the genetic algorithm (GA) and simulated annealing (SA) to strengthen the diversification and intensification phases, respectively. The numerical experiments demonstrate that the hybrid version of WOA and SA returns high-quality solutions and requires an acceptable amount of computational time. The conducted sensitivity analyses underline the importance of tire remanufacturing. Furthermore, setting the appropriate prices in different channels for the available tire types is critical for sustainable tire supply chain management.

关键词： Online-to-offline commerce Dual-channel closed-loop supply chain Uncertainty modelling Tire industry whale optimization algorithm Red deer algorithm

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A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

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SENSORS 2021年第24期21卷 8423-8423页

作者： Hussain, Saddam Mustafa, Mohd Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Saeed, Faisal Al-rimy, Bander Ali Saleh Univ Technol Malaysia Sch Elect Engn Johor Baharu 81310 Malaysia Qassim Univ Coll Comp Dept Comp Sci Buraydah 51452 Saudi Arabia Birmingham City Univ Sch Comp & Digital Technol Birmingham B4 7XG W Midlands England Univ Teknol Malaysia Sch Comp Fac Engn Johor Baharu 81310 Malaysia

This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

关键词： theft detection in power consumption data NGBoost algorithm majority weighted minority oversampling technique algorithm whale optimization algorithm tree SHAP algorithm

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Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104193-104193页

作者： Yousri, Dalia Mirjalili, Seyedali Machado, J. A. Tenreiro Thanikanti, Sudhakar Babu Elbaksawi, Osama Fathy, Ahmed Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Polytech Porto Dept Elect Engn Inst Engn Rua Dr Antonio Bernardino de Almeida 431 P-4249015 Porto Portugal Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Jouf Univ Fac Engn Elect Engn Dept Al Jouf Saudi Arabia Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt Zagazig Univ Fac Engn Elect Power Machine Dept Zagazig Egypt

An effective harmony between the exploration and exploitation phases in meta-heuristics is an essential design consideration to provide reliable performance on a wide range of optimization problems. This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer (HHO) based on the fractional calculus (FOC) memory concept. In the proposed variant of the HHO, a hawk moves with a fractional order velocity, and the rabbit escaping energy is adaptively tuned based on FOC parameters to avoid premature convergence. As a result, the fractional-order modified Harris hawks optimizer (FMHHO) is proposed. The sensitivity of the algorithm performance vis-a-vis the FOC parameters is addressed, and the best variant is recommended based on twenty-three benchmarks. For validating the quality of the proposed variant, twentyeight benchmarks of CEC2017 are tested. For evaluating the proposed variant against the other present-day techniques, several statistical measures and non-parametric tests are performed. Moreover, to demonstrate the applicability of the proposed technique, the proton exchange membrane fuel cell (PEMFC) model parameters estimation process is handled based on several measured datasets. In this series of experiments, the FMHHO variant is compared with the standard HHO and the other techniques based on intensive statistical metrics, mean convergence curves, and dataset fitting. The overall outcome shows that the FOC memory property improves the performance of the classical HHO and leads to accurate and robust solutions fitting the measured data.

关键词： Fractional-order modified HHO whale optimization algorithm Particle Swarm optimization Harris hawks optimization Salp Swarm algorithm Parameters estimation Grey Wolf Optimizer Fractional calculus Genetic algorithm optimization Fuel cell

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A modified salp swarm algorithm (SSA) combined with a chaotic coupled map lattices (CML) approach for the secured encryption and compression of medical images during data transmission

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期66卷 102465-102465页

作者： Selvi, C. Thirumarai Amudha, J. Sudhakar, R. Sri Krishna Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Elect Engn Pollachi Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Commun Engn Pollachi Tamil Nadu India

Medical image data analysis is a significant data in medical systems to analyze patient's health record. The biomedical image processing techniques and modernization of image devices are enhancing the medical image data. The main advantages of medical imaging data are (i) the medical imaging data are transmitted in the secure network in safe from the hackers (ii) medical imaging data can be shared quickly and easily between health care providers and departments. The chaotic system based different encryption algorithms using researches is analyzed. There are numerous issues in transmission of image such as weak security, small key space and high computational time. In this manuscript, a combined coupled map lattice (CML) with modified salp swarm algorithm is proposed. This algorithm compresses and encrypts the image using coupled map lattice (CML). This CML initially generates the number of encrypted images in modified SSA population. After the initialization, the modified SSA with whale optimization algorithm (WOA) is applied to reduce the computational time and maximize entropy in image cipher. To enhance the security of medical image is encrypted as a cipher image and transmits over the network. The experimental outcomes demonstrate that the proposed technique is more efficient to encrypt the medical image and has capability to resist the different distinct attacks.

关键词： Encryption Compression Coupled map lattice Salp swarm algorithm whale optimization algorithm

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Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method

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APPLIED ENERGY 2021年 299卷 117291-117291页

作者： Gu, Bo Shen, Huiqiang Lei, Xiaohui Hu, Hao Liu, Xinyu North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou 450011 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 00038 Peoples R China Hebei Univ Engn Hebei Key Lab Intelligent Water Conservancy Handan 056038 Peoples R China

The primary means to promote grid-connected photovoltaic power generation is through accurately forecasting the power output from photovoltaic power stations. This paper proposes a method for day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis using fuzzy c-means (FCM), whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and non-parametric kernel density estimation (NPKDE). The FCM clustering algorithm was used to cluster historical data on numerical weather prediction and photovoltaic power stations, whereby daily data sharing similar meteorological information were clustered into one class. The rapid convergence speed and high convergence accuracy of the WOA were used to optimize the penalty factor and kernel function width of the LSSVM model;this was done to improve the calculation speed and forecasting accuracy of the LSSVM model. The WOA-LSSVM forecasting model was trained using the clustered numerical weather prediction and historical data of a photovoltaic power station. This was subsequently utilized to forecast day-ahead photovoltaic power. The NPKDE method was used to accurately calculate the probability density distribution of forecasting error and the confidence interval of the day-ahead PPF. The root mean square error (RMSE) values of the forecasting power of the WOA-LSSVM, PSO-LSSVM, LSSVM, LSTM and PSO-BP models are 2.55%, 3.00%, 5.60%, 6.03% and 3.18%, respectively, and the calculation results show that the forecasting accuracy of the WOA-LSSVM was higher relative to other models including PSO-LSSVM, LSSVM, LSTM and PSO-BP. Moreover, the NPKDE method was able to accurately describe the probability density distribution of the forecasting error.

关键词： Fuzzy c-means clustering whale optimization algorithm Least squares support vector machine Photovoltaic power forecasting

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Lifetime improvement of digital microfluidic biochips based on the IWOA

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MICROELECTRONICS RELIABILITY 2021年 123卷 114182-114182页

作者： Shi, Jinlong Fu, Ping Zheng, Wenbin Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China

Digital microfluidic biochips (DMFBs) are increasingly important used for clinical diagnostics drug discovery and point-of-care. Those procedures require high output precision, so the reliability and lifetime of the chips are extremely important. Due to the inherently reconfigurable nature of DMFBs, a degraded electrode may be reused many times. Therefore, the lifetime of the chips is closely related to the total actuation time of an electrode. Thus, the electrode total actuation time needs to be considered carefully in an efficient DMFB design process. This paper proposes an improved whale optimization algorithm (IWOA), which can reduce the excessive use of an electrode and reuse electrodes in an average manner to optimize the longest lifetime of DMFBs. First, the IWOA combined a WOA with a genetic algorithm, and inertial weights were added to enhance the local search ability of the IWOA. Second, the max-min WOA was proposed in the exploration phase. Lastly, the position mass of in-dividual whales was improved to represent the sequence of operations. The simulation experimental results showed that this algorithm was able to solve lifetime optimization problems. The efficiency and convergence performance of the algorithm were very good. The proposed algorithm can achieve maximum improvement in the maximum electrode activation time of 74.6%, 8.2%,15.7% and 7.2% respectively, compared with T-trees algorithm, 3-DDD algorithm, PRSA algorithm and 3D-DDMS algorithm.

关键词： Digital microfluidic chips Lifetime Protein analysis Actuation time whale optimization algorithm

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Evaluating the performance of nature inspired algorithms using 52-bar steel truss subjected to dynamic load

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Materials Today: Proceedings 2021年 38卷 2464-2470页

作者： Saiteja Sistla J.S. Kalyana Rama BITS Pilani-Hyderabad Campus Hyderabad 500078 India Vignana Bharathi Institute of Technology Hyderabad 501301 India

Linear programing problem revolutionized during the world war since it has helped the army to minimize the costs and increase the efficiency in battlefield. Since then optimization techniques have gained popularity in various fields like science & technology, biology, mathematics etc. Nature inspired algorithms mimic the nature’s behavior in order to achieve certain optimization objectives which can achieve productive results to complex problems. Classical algorithms like Genetic algorithm, Particle swarm optimization etc. are widely known but the accuracy of the solution for complex engineering problems is less. Performance evaluation of latest nature inspired algorithms i.e. Moth flame optimizer, Salp Swarm optimizer and whale optimizer is carried out in the present study. These algorithms are proposed recently and they possess salient features like better convergence rate, avoiding the local optimum and robustness, which is the motivation behind choosing these algorithms. A 52-bar steel truss has been chosen for the present study to assess the performance of the chosen optimization techniques. The behavior of steel truss subjected to two different ground motions is also assessed using the three optimization techniques. A comparative study is done to assess the performance of the chosen techniques. MATLAB is adopted for the simulation of chosen problem statement. Based on the results it is observed that Mouth Flame Optimizer has better performance in terms of accuracy, convergence rate and computational time and is suggested for various types of mechanical and structural problems involving 52-bar trusses.

关键词： Moth flame optimizer Salp Swarm algorithm whale optimization algorithm MATLAB Steel truss

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Predicting Chinese total retail sales of consumer goods by employing an extended discrete grey polynomial model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104261-104261页

作者： Liu, Chong Xie, Wanli Wu, Wen-Ze Zhu, Hegui Northeastern Univ Sch Sci Shenyang 110819 Peoples R China Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China

The total retail sales of consumer goods is not only an important indicator to measure the consumption level of the Chinese people, but also an important indicator of the national economy. Therefore, it is of great significance to analyze the development trend of Chinese total retail sales of consumer goods for the healthy development of Chinese economy. To explore the future development trend of Chinese total retail sales of consumer goods, this paper develops an extensive discrete grey model by the introduction of weighted fractional accumulation and discretization error, which is abbreviated as WFDPGM(1,1,t(alpha)) model. To further enhance the prediction performance of the proposed model, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients. Three real cases are used to verify the effectiveness of the proposed model by comparing with other competing models. Lastly, based on Chinese retail sales of consumer goods from 2005 to 2019, the numerical results show that the proposed model outperforms other benchmarks, and the future development of Chinese retail sales of consumer goods will maintain an increasing trend, reaching 533033.15 x 10(9) yuan in 2025. And some managerial insights are obtained from numerical examples.

关键词： Retail sales of consumer goods Weighted fractional accumulation Discretization technique whale optimization algorithm

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A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China

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AGRICULTURAL WATER MANAGEMENT 2021年 244卷 106594-106594页

作者： Yan, Shicheng Wu, Lifeng Fan, Junliang Zhang, Fucang Zou, Yufeng Wu, You Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China

The information of reference evapotranspiration (ET0) is vital for optimizing irrigation scheduling, planning water resources and assessing hydrological drought. However, accurate estimation of ET0 is difficult if long-term or complete climatic variables are unavailable, especially in developing countries like China. This study proposed a novel hybrid extreme gradient boosting (XGB) model with the whale optimization algorithm (WOA) to estimate daily ET0 at four stations in the arid region and four stations in the humid region of China. Particularly, its performances were evaluated under the local and three external scenarios with seven incomplete combinations of maximum and minimum temperatures (T-max and T-min), relative humidity (RH), wind speed (U-2), relative sunshine duration (n/N) and extra-terrestrial radiation (R-a) for the period 1966-2015. The results showed that U-2 was the most influencing variable for daily ET0 estimation in the arid region, followed by n/N and RH, while n/N was more important than RH and U-2 in the humid region. Locally trained and tested WOA-XGB models greatly outperformed their corresponding simplified FAO-56 PM models, with the average decrease in root mean square error (RMSE) by 40.1% and 38.9% in the arid and humid regions, respectively. Compared with local WOA-XGB models, the prediction accuracy of externally trained WOA-XGB models with local or external testing data decreased by 18.1% or 69.9% in the arid region, and 16.8% or 67.9% in the humid region, respectively. However, external WOA-XGB models with synthetic testing data from the target and adjacent stations overall improved the prediction accuracy of local WOA-XGB models by 5.7% and 9.6% in the arid and humid regions, respectively. The results indicated that external WOA-XGB models with local testing data produced acceptable daily ET0 estimates. However, when synthetic data were employed during testing, external WOA-XGB models gave excellent daily ET0 estimates, which were

关键词： Reference evapotranspiration Extreme gradient boosting whale optimization algorithm Cross station FAO-56 Penman-Monteith

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