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Short-term wind speed prediction based on FEEMD-PE-SSA-BP

ENVIRONMENTAL SCIENCE AND POLLUTION REsearch 2022年第52期29卷 79288-79305页

作者： Zhu, Ting Wang, Wenbo Yu, Min Wuhan Univ Sci & Technol Sch Sci Wuhan 430081 Peoples R China

As one of the renewable energy power generation methods, wind power generation shows a high growth trend. However, while wind power is connected to the grid, the volatility and instability of wind power make the power system produce a lot of uncertain fluctuations, which greatly affects the power quality and jeopardizes the stable operation of the power system. Therefore, high wind speed forecasting accuracy can provide a solid basis for grid management, improve the power system's ability to consume wind power, and ensure the safety and stabilization of the power system. In order to solve the problem of inaccurate prediction caused by the non-linearity and unsteadiness of wind speed series, this paper proposes a Fractal Ensemble Empirical Mode Decomposition (FEEMD)-Permutation Entropy (PE)-sparrow search algorithm (SSA)-Error Back Propagation (BP) neural network method for short-term wind speed prediction. This method first uses FEEMD to decompose the original wind speed in order from high to low frequency;then calculates the entropy value of each component, and merges the components with similar entropy values to effectively reduce the computation;and finally, the new sub-series are predicted by SSA-BP model, and the predicted value of the merged new sub-sequences are accumulated to obtain the final wind speed prediction results. The simulation study shows that the proposed prediction model is not only fast and accurate, but also suitable for short-term wind speed prediction.

关键词： Wind speed prediction sparrow search algorithm Permutation entropy BP neural network FEEMD

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Short-Term Power Load Forecasting Based on CNN-BiLSTM-Attention Neural Network with ISSA Optimized 5

Short-Term Power Load Forecasting Based on CNN-BiLSTM-Attent...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Wei, Xiang Zhou, Yang Bai, Yaxin Li, Zihui Hu, Mingqi Liu, Qiuming State Grid Jiangxi Elettric Power co. Ltd Nanchang China Information and Communication Branch of State Grid Jiangxi Electric Power Co. Ltd. Nanchang China School of Software Engineering Jiangxi University of Science and Technology Nanchang China Advanced Copper Industry College Jiangxi University of Science and Technology Yingtan China

ISBN: (纸本)9798350355925

Accurate short-term electricity load forecasting is essential to ensure the safe, reliable, and economically efficient operation of power systems. However, traditional forecasting methods are increasingly inadequate to meet the demand for ever more precise load predictions. In response to this issue, a CNN-BILSTM-Attention neural network method optimized based on an improved sparrow search algorithm is proposed for short-term electricity load forecasting. Firstly, the Sin chaotic mapping strategy is utilized to initialize the sparrow population, enhancing the diversity of population positions. Additionally, the cosine optimization algorithm is introduced into the formula for updating the joiner's position. This aims to address the decrease in global search capability in the later iterations of the sparrow algorithm, thereby enhancing the accuracy of the sparrow search algorithm;Next, the ISSA algorithm is employed to optimize the CNN-BILSTM-Attention neural network. This helps avoid overly random initialization of the CNN-BILSTM-Attention neural network's initial weight thresholds, thus enhancing the stability of prediction results;Finally, load forecasting is conducted and compared with other algorithmic models for analysis. Additionally, for the WOA-CNN-BILSTM-Attention model, RMSE decreases by 1.832%, with a corresponding MAE reduction of 1.073%. Similarly, for the PSO-CNN-BILSTM-Attention model, RMSE decreases by 1.804% and MAE by 1.026%. The results of the case study demonstrate that the proposed model can effectively enhance prediction accuracy and is valid for short-term load forecasting. By accurately predicting the power demand, the reasonable distribution and optimal dispatch of power can be realized, and the power grid fluctuations caused by the load mutation can be reduced, so as to ensure the quality of power supply. © 2024 IEEE.

关键词： CNN-BILSTM-Attention ISSA Load Forecasting sparrow search algorithm

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NDDSM: Novel Deep Decision-Support Model for Hate Speech Detection

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SN Computer Science 2024年第1期5卷 67页

作者： Kumar, Ashwini Kumar, Santosh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

The pervasiveness of social media in people's lives is indisputable, the issue where it has become a necessary part of daily practices. However, unrestricted access to social media allows anonymous individuals to spread meaningless or even hostile information, defeating communication's purpose. Social media’s positive and negative impact on society or individuals becomes more pronounced as its usage increases. As the harmful effect of unmonitored ‘hate speech’ becomes increasingly apparent, detecting such content has become a crucial concern in social media. In a recent study, machine-learning models have been developed to identify hate speech across multiple languages. As a result, the use of Bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) for feature extraction in evaluating and identifying hate speech has risen. However, LSTM and CNN hyperparameters are typically selected based on expert opinion and prior research, making it difficult for the model to generalize since its creators need to know the optimal values for its parameters. To address this issue, we propose a novel deep decision support model which uses the sparrow search algorithm (SSA) to optimize the Bi-LSTM and CNN model hyperparameters for detecting hate speech. We employed the SSA for the decision support system to identify the best hyperparameters for the model architecture to improve its interpretability and accuracy. The benchmark datasets have been used to evaluate the model's performance, and the results indicate that our proposed model outperforms conventional hate speech detection systems. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Decision support system Hate speech Social media sparrow search algorithm

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Air quality prediction based on ICEEMDAN-SSA-LSTM 36

Air quality prediction based on ICEEMDAN-SSA-LSTM

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36th Chinese Control and Decision Conference (CCDC)

作者： Qin, Yanling Ma, Hongyan He, Wei Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Inst Distributed Energy Storage Safety Big Data Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

To address the problem that the Air Quality Index (AQI) is difficult to predict, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), sparrow search algorithm (SSA), and Long Short-Term Memory (LSTM) is proposed. First, the AQI sequence was decomposed by ICEEMDAN. Second, the initial learning rate, max training epoch and the number of hidden layers of the LSTM model are optimized by the SSA algorithm to establish the SSA-LSTM model. Last, the results decomposed were imported into the SSA-LSTM model to predict AQI. Compared with the prediction results of the LSTM, CEEMD-LSTM, and CEEMDAN-SSA-LSTM model, the accuracy (measured by correlation coefficients) of the CEEMDAN-SSA-LSTM is 98%. The result confirmed that the ICEEMDAN-SSA-LSTM can obtaining an improvement of the correlation coefficients and reduce the mean square error, which is valuable for engineering applications.

关键词： Air quality prediction Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise sparrow search algorithm Long and short-term memory

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Multi-Joint Predictor for Human Upper Limbs in Human-Robot Collaboration Tasks 10

Multi-Joint Predictor for Human Upper Limbs in Human-Robot C...

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10th International Conference on Electrical Engineering, Control and Robotics (EECR)

作者： Zhang, Mengyao Zhang, Zijian Wang, Linjian Li, Qinghua Feng, Chao Shandong Acad Sci Qilu Univ Technol Sch Informat & Automat Jinan Peoples R China Shandong Acad Sci Qilu Univ Technol Int Sch Optoelect Engn Jinan Peoples R China Shandong Acad Med Sci Shandong First Med Univ Coll Artifificial Intelligence & Big Data Med Sci Jinan Peoples R China

ISBN: (纸本)9798350370010;9798350370003

The safety of collaborators is the primary concern in human-robot collaboration tasks. Accurately perceiving the human body pose information is a crucial technology for achieving the safe execution of human-robot collaboration. This paper proposes a multi-joint predictor for the human upper limb, discusses the correlation between multiple joints and the predictor's performance under different motion speeds to provide precise obstacle avoidance information for collision detection. A combined dataset is created by collecting operation information from medical personnel in a cellular experiment context. Secondly, the sparrow search algorithm is utilized to optimize the Long Short Term Memory network for constructing a predictor for multiple joints while discussing the correlation between each joint to obtain real-time position updates. Finally, the usability and accuracy of the proposed predictor is validated.

关键词： human-robot collaboration trajectory prediction sparrow search algorithm Long Short Term Memory

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Improved Particle Swarm Based on Elastic Collision for DNA Coding Optimization Design

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IEEE ACCESS 2022年 10卷 63592-63605页

作者： Zhu, Donglin Huang, Zuwei Xie, Linpeng Zhou, Changjun Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321017 Zhejiang Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China

In DNA computing, the design of DNA coding sequences is an important factor affecting the reliability of DNA computing. In different DNA sequence designs, suitable constraints should be selected and the sequence design should be rationalized according to these constraints. In this paper, an improved particle swarm optimization algorithm based on elastic collision strategy (EC-PSO) is used to optimize the design of DNA sequences by using an adaptation function that satisfies multiple constraints. EC-PSO uses the idea of elastic collision to improve the optimal and worst positions within the population, introduces the flight means of the sparrow search algorithm (SSA) to enhance the search capability of the algorithm and increase the diversity of the population;then introduces the harmony search algorithm to the population is then fine-tuned to improve the quality of the solution. The effectiveness of the algorithm was verified by comparing it with the other six algorithms in eight test functions. Finally, the sequence designed was more reasonable in the DNA optimal design experiment.

关键词： DNA Optimization Encoding Particle swarm optimization Image coding Statistics Sociology DNA computing DNA coding improved particle swarm of elastic collisions sparrow search algorithm harmony search algorithm

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A novel blood glucose time series prediction framework based on a novel signal decomposition method

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CHAOS SOLITONS & FRACTALS 2022年第0期164卷

作者： Zhu, Ting Wang, Wenbo Yu, Min Wuhan Univ Sci & Technol Sch Sci Wuhan 430081 Peoples R China

Unstable blood glucose levels will bring about a series of complications, and even threaten our life in severe cases. Therefore, accurately predicting and controlling blood glucose has become an indispensable part of diabetes treatment. For the sake of boosting the accuracy of blood glucose prediction, a novel time-frequency signal analytical technology called improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed to decompose the nonlinear and non-stationary raw blood glucose series to obtain intrinsic mode functions (IMFs) and residual components (Rs) in different frequency bands. Then, in order to overcome the problem that the kernel extreme learning machine (KELM) is easy to fall into the local optimum, the sparrow search algorithm (SSA) is introduced to further enhance the robustness and stability of KELM model. Ultimately, the predictions of each optimized KELM are fused to achieve the final prognostic results. Through the experimental results of three patients, the following contributions can be summarized: (a) FEEMD exploits the box-counting dimension to remove abnormal signals in the original sequence, which guarantees the completeness of decomposition and obtains better decomposition results;(b) SSA is exploited to optimize the KELM, which can significantly boost the prediction effects;(c) When compared with the other seven benchmark models, the proposed framework in this paper is more suitable for predicting blood glucose and has higher prediction accuracy;(d) This paper also verifies that the proposed architecture can effectively reduce the false negative rate and increase the overall alarming accuracy in early warning of hypoglycemia.

关键词： Blood glucose prognosis sparrow search algorithm Kernel -based extreme learning machine Fractal dimension Ensemble empirical mode decomposition Hypoglycemic warning

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Power system equivalent inertia prediction based on EMD-SSA-LSTM and system partitioning 7

Power system equivalent inertia prediction based on EMD-SSA-...

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Bian, Hui Yang, Qiaoling Zhang, Qi Su, Binhua Zhang, Boyan Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9798400708305

Power system inertia level forecast in advance is a crucial need for the reliable functioning of power systems. A power system equivalent inertia prediction model based on the EMD-SSA-LSTM network with system partitioning is suggested in order to increase the accuracy of power system inertia prediction. Firstly, the system is partitioned using K-Medoide, and the dominant generator in each region is selected according to the partitioning basis, then the prediction model of the system operation and system equivalent inertia for the dominant generator, new energy generators, etc. is established, and the hyper-parameters of the LSTM algorithm are optimized by SSA through the EMD after the data processing, and finally, the Pearson correlation coefficients are matched with the appropriate prediction model. Finally, the prediction of the equivalent inertia of the power system is completed, and the results show that the prediction accuracy of EMD-SSA-LSTM based on the system partition is higher than that of other algorithms, its prediction accuracy is within 99%, which effectively improves the accuracy of the prediction of the equivalent inertia of the power system.

关键词： System Partitions Empirical Mode Decomposition sparrow search algorithm Long Short-Term Memory Equivalent Inertia Prediction

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Ship Attitude Prediction Based on Dynamic Sliding Window and EEMD-SSA-BiLSTM 32nd

Ship Attitude Prediction Based on Dynamic Sliding Window and...

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Wang, Jiaqi Chen, Yaojie Wuhan Univ Sci & Technol Dept Comp Sci & Technol Wuhan Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Peoples R China

ISBN: (纸本)9783031442001;9783031442018

A ship attitude prediction method is proposed in this paper, which combines dynamic sliding window, sparrow search algorithm (SSA), Ensemble Empirical Mode Decomposition (EEMD), and Bidirectional Long Short-Term Memory (BiLSTM). This method addresses the highly random, non-stationary, and non-linear characteristics of ship motion on the sea surface, making it difficult to accurately predict its motion state. Firstly, the EEMD method is used to decompose the ship attitude into several Intrinsic Mode Functions (IMF) and a Residual (Res) to reduce noise and non-smoothness in the ship attitude data. Secondly, the corresponding sliding window size and sliding step length are calculated for each IMF component, and the dynamic sliding window is used to extract the local features of the data, reducing the high randomness influence of the ship motion attitude. Finally, the SSA algorithm is used to find the optimal parameters in the BiLSTM network to improve the accuracy of the prediction method. The experimental results show that the EEMD-SSA-BiLSTM model incorporating dynamic sliding windows significantly improves prediction accuracy and generalization ability compared to other models.

关键词： Ship attitude prediction Neural Networks Dynamic sliding window Ensemble Empirical Mode Decomposition sparrow search algorithm

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A swarm intelligence approach for energy management of grid-connected microgrids with flexible load demand response

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INTERNATIONAL JOURNAL OF ENERGY REsearch 2022年第4期46卷 4301-4319页

作者： Singh, Arvind R. Ding, Lei Raju, Dhenuvakonda Koteswara Raghav, Lolla Phani Kumar, Rangu Seshu Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China Natl Inst Technol Dept Elect Engn Silchar India

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

关键词： comprehensive load-responsive model critical peak pricing energy management system flexible price elasticity microgrid real-time pricing sparrow search algorithm time of use

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