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Forecasting of the Short-Term Electricity Load Based on WOA-BILSTM

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第11期37卷

作者： Zhao, Huaxin Zhou, Zhenliu Zhang, Pizhen Shenyang Inst Engn Elect Engn Coll Shenyang Peoples R China Shenyang Key Lab Energy Internet Intelligent Perce Shenyang Peoples R China Shenyang Inst Engn Informat Coll Shenyang Peoples R China

One of the biggest difficulties, which China's power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data's short-term predictions. At the same time, it also reduces the time of personnel debugging.

关键词： Short-term power load forecasting machine learning whale optimization algorithm neural network with dual long-short-term memory

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Inadequate load output diagnosis of ultra-supercritical thermal power units based on MIWOA multi-label random forest

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APPLIED THERMAL ENGINEERING 2023年 227卷

作者： Tang, Mingzhu Liang, Zixin Ji, Dongxu Yi, Jiabiao Peng, Zhonghui Huang, Yujie Wang, Jiachen Chen, Donglin Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Peoples R China

Aiming at the problem of considerable economic losses caused by the large fluctuation range of response load and Automatic Generation Control (AGC) commanded load of ultra-supercritical thermal power units, an inadequate load output diagnosis model of multi-label random forest with multi-improved whale optimization algorithm (MIWOA-MLRF) is proposed. Thermal power units are a kind of high-dimensional, nonlinear, and complex industrial complex, which brings difficulty for conventional mechanistic models in comprehensively analyzing the inadequate output causes of the units. With the help of extensive data analysis and artificial in-telligence algorithms, a multi-label random forest (MLRF) for inadequate output cause analysis is constructed. To improve the accuracy of WOA for MLRF classification, a good point set strategy is used to optimize the initial population distribution of WOA, and an improved convergence factor is used to control the speed of WOA search. Three Gaussian simulation datasets with a different number of features and labels and three sets of thermal power operation data with varying periods are utilized. The test results show that the missing alarm rates (MAR) of MIWOA-MLRF are 0.301, 0.621, and 0.802 under three sets of Gaussian data tests, which are the smallest values among all analytical models. Under three sets of thermal power unit operation data, the average values of MIWOA-MLRF's MAR are 2.2%, 0.2%, and 1.5%, respectively, which have lower missing alarm rates compared with other algorithms. Furthermore, in the false alarm rate (FAR) comparison, the FAR of MIWOA-MLRF de-creases significantly compared to MLRF. In the optimization time comparison, the optimization time required for MIWOA-MLRF is about 1000 s, which saves 33% on average compared to the optimization time of other algo-rithms. The result indicates that MIWOA-MLRF can guarantee the same better real-time performance with reduced FAR and MAR of inadequate output cause classification.

关键词： Thermal power units Inadequate output Multi-label classification whale optimization algorithm Random forest

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Multiple Unmanned Aerial Vehicle Autonomous Path Planning algorithm Based on whale-Inspired Deep Q-Network

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DRONES 2023年第9期7卷

作者： Wang, Wenshan Zhang, Guoyin Da, Qingan Lu, Dan Zhao, Yingnan Li, Sizhao Lang, Dapeng Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.

关键词： deep reinforcement learning deep Q-network whale optimization algorithm multi-UAV path planning

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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 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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An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel-Rail Coupled Fault Diagnosis for Rail Vehicles

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SENSORS 2023年第12期23卷 5486页

作者： Xu, Zihang Yang, Jianwei Yao, Dechen Wang, Jinhai Wei, Minghui Beijing Univ Civil Engn & Architecture Beijing Key Lab Performance Guarantee Urban Rail T Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel-rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel-rail coupled faults will improve the safety of rail vehicles' operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel-rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time-frequency analysis method is proposed in this paper based on the PDMF adopting Renyi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Renyi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles' variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

关键词： rail vehicles parameterized domain mapping wheel-rail coupled fault fault diagnosis whale optimization algorithm

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Research on Short-Term Electric Load Forecasting Using IWOA CNN-BiLSTM-TPA Model

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印刷与数字媒体技术研究 2025年第1期 179-187页

作者： MEI Tong-da SI Zhan-jun ZHANG Ying-xue College of Artificial Intelligence Tianjin University of Science and TechnologyTianjin 300457China

Load forecasting is of great significance to the development of new power *** the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and *** places higher demands on the prediction and analysis of power *** order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved whale optimization algorithm(IWOA)with mixed strategies was ***,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data ***,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the *** allowed the model to differentiate the importance of load sequences at different time *** the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the *** this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.

关键词： whale optimization algorithm Convolutional Neural Network Long Short-Term Memory Temporal Pattern Attention Power load forecasting

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Hybridization of Galactic Swarm and Evolution whale optimization for Global Search Problem

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IEEE ACCESS 2020年 8卷 74991-75010页

作者： Binh Minh Nguyen Trung Tran Thieu Nguyen Giang Nguyen Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam Slovak Acad Sci Inst Informat Bratislava 84507 Slovakia

The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the whale optimization algorithm. Concretely, the exploitation phase of Galactic Swarm optimization is replaced by our Evolution whale optimization algorithm to avoid early convergence. The whale optimization algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.

关键词： optimization whales Particle swarm optimization Search problems Sociology Statistics MIMICs Global optimization hybridization evolution computation galactic swarm optimization 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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Novel Mathematical Design of Triple-Tuned Filters for Harmonics Distortion Mitigation

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ENERGIES 2023年第1期16卷

作者： Maher, Mohamed Abdel Aleem, Shady H. E. Ibrahim, Ahmed M. M. El-Shahat, Adel Cairo Univ Fac Engn Elect Power & Machines Dept Cairo 12613 Egypt Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt Purdue Univ Sch Engn Technol Energy Technol Program W Lafayette IN 47907 USA

The design of AC filters must meet the criteria of harmonic distortion mitigation and reactive power support in various operating modes. The stringent reactive power-sharing requirements currently lead to sophisticated filter schemes with high component ratings. In this regard, triple-tuned filters (TTFs) have good potential in harmonic mitigation of a broad range of harmonics. In the literature, the TTF design has been presented using a parametric method, assuming that the TTF is equivalent to a three-arm single-tuned filter (TASTF). However, no direct methods of designing it or finding its optimal parameters have been provided. This paper presents novel mathematical designs of TTFs. Three different design methods are considered-the direct triple-tuned filter (DTTF) design method, as a TASTF, and a method based on the equivalence between the two design methods called the equivalence hypothesis method to design the triple-tuned filter (EHF). The parameters of the three proposed design methods are optimized based on the minimization of a proposed multi-objective function using a recent metaheuristic algorithm called artificial rabbits optimization (ARO) to mitigate harmonics, improve power quality, and minimize power losses in an exemplary system presented in IEEE STD-519. Further, the system's performance has been compared to the system optimized by the ant lion optimizer (ALO) and whale optimization algorithm (WOA) to validate the effectiveness of the proposed design. Simulation results emphasized harmonics mitigation in the system, the system losses reduction, and power quality improvement with lower reactive power filter ratings than conventional single and double-tuned filters.

关键词： triple-tuned filter passive filter power quality harmonics optimization artificial rabbits optimization ant lion optimizer whale optimization algorithm

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Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators under Kinematic Constraints

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SENSORS 2023年第6期23卷 3074页

作者： Liu, Tianyu Cui, Jingkai Li, Yanhui Gao, Siyuan Zhu, Mingchao Chen, Liheng Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys CAS Key Lab On Orbit Mfg & Integrat Space Opt Syst Changchun 130033 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.

关键词： asymmetric S-curve velocity scheduling trajectory planning redundant manipulators whale optimization algorithm kinematic constraints

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