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VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy

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RENEWABLE ENERGY 2024年 236卷

作者： Xu, Yuzhen Huang, Xin Zheng, Xidong Zeng, Ziyang Jin, Tao Fuzhou Univ Dept Elect Engn Fuzhou 350116 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

Electricity price prediction is essential for the optimal dispatch in power markets, with accurate prediction models being critical for efficient power system operations and market trading decisions. Deep learning networks, with their powerful nonlinear modeling capabilities, have shown promising results in electricity price forecasting. However, their design techniques, especially the selection of network parameters, remain challenging. This indicates that the optimization and exploration of deep learning networks in electricity price forecasting models require further investigation. This paper innovatively proposes a forecasting model that uniquely integrates Variational Mode Decomposition (VMD), grey wolf optimization (GWO), Attention Mechanism (ATT), and Long Short-Term Memory Network (LSTM), optimizing the model from three different perspectives. First, during the data preprocessing phase, the training set is subjected to VMD to reduce noise, thereby enhancing the capture of multi-scale characteristics inherent in electricity price time series. The ATT layer is integrated to adaptively allocate weights, enhancing the model's focus on significant features. The GWO is applied to optimize hyperparameters of the LSTM, accelerating convergence and improving iteration accuracy, thereby reducing model error. A series of experiments were conducted using multiple regional electricity price datasets, evaluated with several metrics including RMSE. The results validated the effectiveness of the proposed three modules in improving the performance of the time series network, with VMD making the most significant contribution. Among all models, VMD-GWO-ATT-LSTM consistently outperformed others, demonstrating its effectiveness and robustness in electricity price forecasting.

关键词： Electricity price prediction Long-short term memory Attention mechanism grey wolf optimization algorithm Variational mode decomposition

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A Velocity-Guided grey wolf optimization algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification

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IEEE ACCESS 2024年 12卷 39887-39901页

作者： Zhang, Li Chen, Xiaobo Hainan Normal Univ Key Lab Data Sci & Intelligence Educ Minist Educ Hainan 571158 Peoples R China Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Jiangsu Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and data mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. The grey wolf optimization (GWO) algorithm is a novel intelligent population algorithm. Simple principles and few parameters characterize it. However, the basic GWO has disadvantages, such as difficulty coordinating exploration and exploitation capabilities and premature convergence. As a result, GWO fails to identify many irrelevant and redundant features. To improve the performance of the basic GWO algorithm, this paper proposes a velocity-guided grey wolf optimization algorithm with adaptive weights and Laplace operators (VGWO-AWLO). Firstly, by introducing a uniformly distributed dynamic adaptive weighting mechanism, the control parameters $a$ are guided to undergo nonlinear dynamic changes to achieve a good transition from the exploratory phase to the development phase. Second, a velocity-based position update formula is designed with an individual memory function to enhance the local search capability of individual grey wolves and drive them to converge to the optimal solution. Thirdly, a Laplace cross-operator strategy is applied to increase the population diversity and help the grey wolf population escape from the local optimal solution. Finally, the VGWO-AWLO algorithm is evaluated for its comprehensive performance in terms of classification accuracy, dimensionality approximation, convergence, and stability in 18 classified datasets. The experimental results show that the classification accuracy and convergence speed of VGWO-AWLO are better than the basic GWO, GWO variants, and other state-of-the-art meta-heuristic algorithms.

关键词： Reflective binary codes Statistical analysis Social factors Machine learning algorithms Classification algorithms Approximation algorithms Laplace equations Heuristic algorithms Nonlinear dynamical systems Metaheuristics grey wolf optimization algorithm feature selection dynamic adaptive weighting mechanism velocity update equation mechanism Laplace operators

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Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 18-42页

作者： Sefati, SeyedSalar Mousavinasab, Maryamsadat Zareh Farkhady, Roya Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Inst Higher Educ Roshdiyeh Dept Comp Engn Tabriz Iran

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal.

关键词： Cloud computing Load balancing grey wolf optimization algorithm Reliability

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Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic-grey wolf optimization algorithm

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IEEE SENSORS JOURNAL 2018年第16期18卷 6804-6811页

作者： Daniel, Ebenezer Vignans Fdn Sci Technol & Res Dept Elect & Commun Engn Guntur 522213 India

Medical image fusion techniques have been widely used in various clinical applications. Generalized homomorphic filters have Fourier domain features of input image. In multi-modal medical image fusion discrete wavelet transform-based techniques provides more features and is performed over Fourier spectrum. In this paper, we proposed a homomorphic wavelet fusion which is called optimum homomorphic wavelet fusion (OHWF) using hybrid genetic-grey wolf optimization (HG-GWO) algorithm. In OHWF, which consists of logarithmic and wavelet domain information of input images. The wavelet-based homomorphic fusion consists of multilevel decomposition features of input image. In our proposal, the approximation coefficients of modality1 (anatomical structure) and optimum scaled detailed coefficients of modality2 are given to adder1. In adder 2, the optimum scaled detailed coefficients of modality 1 and approximation coefficients of modality 2 are added together. The resultants of adder 1 and adder 2 are fused together using pixel based averaging rule. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT, and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with genetic operator. Experimental results show that the proposed approach outperforms state-of-the-art fusion algorithms in terms of both structural and the functional information in the fused image.

关键词： Homomorphic filtering wavelet transform image fusion medical imaging grey wolf optimization algorithm genetic algorithm

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Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第3期15卷 409-417页

作者： Huang, Xinyi Huang, Xiaoli Wang, Binrong Xie, Zhenyu Xihua Univ Res Inst Signal Detect & Informat Proc Technol 9999 Hongguang Ave Chengdu 610039 Peoples R China Fribourg Univ Dept Phys Ave Europe 20 CH-1700 Fribourg Switzerland

Power transformers are important pieces of equipment for the operation of power systems. Accurate diagnosis of their fault is closely related to the stable operation of the entire power grid. In order to improve the diagnostic accuracy of transformer fault, the grey wolf optimization (GWO) algorithm is introduced, and the differential evolution mechanism is integrated into the algorithm. Therefore, this paper proposes a transformer fault diagnosis method based on the modified grey wolf optimization algorithm (MGWO) and support vector machine (SVM), so that the application method realizes optimization of the penalty factor and the kernel parameter in SVM. Through the analysis of existing data examples, the SVM model optimized by the MGWO algorithm has the advantages of good generalization and strong predictive ability, and its fault diagnostic accuracy is higher than those of the genetic algorithm, particle swarm optimization algorithm, and GWO algorithm. This method has practical application significance. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： power transformer fault diagnosis grey wolf optimization algorithm differential evolution support vector machine

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High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

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SOFT COMPUTING 2020年第21期24卷 16691-16704页

作者： Pazhaniraja, N. Sountharrajan, S. Kumar, B. Sathis VIT Bhopal Univ Sch Comp Sci & Engn Sehore Madhya Pradesh India VIT Univ Sch Comp Sci & Engn Chennai Tamil Nadu India

In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed;the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance.

关键词： High utility itemset Boolean operators grey wolf optimization algorithm

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Training Sigma-Pi neural networks with the grey wolf optimization algorithm

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GRANULAR COMPUTING 2023年第5期8卷 981-989页

作者： Sarikaya, Cansu Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Artificial neural network models have been frequently used in time series forecasting problems as an alternative to many classical forecasting models. Although multi-layer perceptron neural networks are one of the most frequently used artificial neural networks in the literature, high-order neural networks using high-order combinations of inputs have superior performance compared to multi-layer perceptron artificial neural networks in recent years. Although there are many high-order artificial neural networks with different properties in the literature, one of the most important problems of these high-order artificial neural networks is to determine the optimization method to be used in the training of the network structure. Sigma-Pi artificial neural networks, one of the high-order artificial neural networks, have been used frequently in many problems in recent years. Like many artificial neural networks, the training of the Sigma-Pi neural network is one of the important factors affecting the performance of the network. In this study, the grey wolf optimization algorithm is used for the first time in the literature in the training of Sigma-Pi artificial neural networks. Thus, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. In the evaluation of the performance of the proposed method, the closing prices of the FTSE and S&P 500 are analyzed for different years. According to the analysis results, the proposed method has a 60% success rate for both FTSE and S&P 500 time series. For the comparison of all methods, the mean rank calculation is made for each method. The proposed method took first place in this ranking and is determined as the best method among all methods.

关键词： Sigma-Pi artificial neural networks grey wolf optimization algorithm optimization Forecasting

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Fault Location of Active Distribution Network Based on Differential Evolution grey wolf optimization algorithm 10

Fault Location of Active Distribution Network Based on Diffe...

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10th International Conference on Power Electronics Systems and Applications (PESA)

作者： Mo, Xin Han, Liguo Wu, Lilin Tang, Wentao Li, Jingming Liu, Junfeng Fan, Junhao Guangdong Power Grid Co Ltd Zhanjiang Power Supply Bur Zhanjiang Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350365818;9798350365801

After distributed power generations are connected to the power grid, the original fault location algorithm is not applicable, and the intelligent algorithm may fall into the local optimal solution. To solve these problems, this paper adopt a differential evolution grey wolf optimizer (DE-GWO) to locate faults in active distribution networks. First, the algorithm introduces the tent chaotic mapping for the high quality of initial population generation. Next, the differential evolution (DE) process is used to generate a mutated population and select individuals with high fitness for subsequent calculation to improve the algorithm's global search. Finally, the proposed method is simulated on the IEEE33-bus active distribution network model. It is verified that the proposed DE-GWO algorithm is faster and more accurate than the original GWO algorithm under both single fault and multiple faults, which can achieve accurate positioning.

关键词： Active distribution network fault location grey wolf optimization algorithm differential evolution algorithm

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Multi-UAV Three-Dimensional Path Planning Based on Improved grey wolf optimization algorithm 43

Multi-UAV Three-Dimensional Path Planning Based on Improved ...

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43rd Chinese Control Conference (CCC)

作者： Zhang, Mingyu Liu, Feng Wang, Yujie Yan, Yaqing Wei, Zheng Xian Beijing Jiaotong Univ Inst Adv Control Syst Beijing 100044 Peoples R China China State Shipbldg Corp Syst Engn Res Inst Beijing 100094 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

To address the challenges of multi-unmanned aerial vehicle (UAV) trajectory planning in three-dimensional complex environments. this study proposes a method based on the Improved grey wolf optimization algorithm for Multi-UAV 3D Trajectory Planning. The approach simulates real geographical environments, establishing three-dimensional terrain and no-fly zone models. Building upon the foundation of single UAV trajectory planning, the proposed method incorporates collaborative constraints for multi-UAV coordination, forming an evaluation function for multi-UAV collaborative trajectory planning. In order to solve the limitations of the standard grey wolf algorithm, which is prone to local optima and exhibits suboptimal convergence rates, an improved convergence factor strategy and a reward-penalty mechanism in the optimization process are introduced. Comparative evaluations against several relevant algorithms validate the superior feasibility of the proposed approach. Simulation results demonstrate that, compared to other algorithms. the proposed method achieves smaller trajectory costs, faster convergence rates, and more stable performance.

关键词： grey wolf optimization algorithm Multi-UAV 3D Trajectory Planning Convergence Factor Reward-Penalty Mechanism

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Path Planning of UAV for Oilfield Inspection Based on Improved grey wolf optimization algorithm 31

Path Planning of UAV for Oilfield Inspection Based on Improv...

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31st Chinese Control And Decision Conference (CCDC)

作者： Ge, Fawei Li, Kun Xu, Wensu Wang, Yi'an Bohai Univ Coll Engn Jinzhou 121013 Peoples R China

ISBN: (纸本)9781728101057

It is difficult for the traditional manual inspection method to satisfy the current management requirements. Now, UAV inspection technology has been used by more and more enterprises. In the UAV inspection, path planning is an important work. An improved grey wolf optimization algorithm is proposed for the path planning of UAV in oilfield environment in this paper. Firstly, the model of the oilfield environment is built;secondly, the initial path is produced by the basic grey wolf optimization (GWO) algorithm;and then, the fruit fly optimization (FOA) algorithm is used to continue the local optimization of the optimal solution;finally, the optimal path is generated. Compared with some other methods, the simulation results show that the improved grey wolf optimization algorithm is effective.

关键词： Oilfield inspection UAV Path planning grey wolf optimization algorithm Fruit fly optimization algorithm

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