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GRU combined model based on multi-objective optimization for short-term residential load forecasting

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10423-10440页

作者： Yi, Lingzhi Peng, Xinlong Fan, Chaodong Wang, Yahui Li, Yunfan Liu, Jiangyong College of Automation and Electronic Engineering Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan China School of Computer Science and Technology Hainan University Hainan China College of Electrical and Information Engineering Hunan University Changsha China

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. © 2024 - IOS Press. All rights reserved.

关键词： multiobjective optimization

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multi-objective Optimal Scheduling of Stacker–reclaimers Using the RPC-NSGA II Algorithm

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Recent Patents on Mechanical engineering 2023年第3期16卷 222-234页

作者： Yi, Lingzhi Liu, Qiankun Wang, Yahui Zhang, Huiting Peng, Xinlong College of Automation and Electronic Engineering Xiangtan University & Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

Background: The stacker-reclaimer is a device for transporting bulk materials in ironmaking raw material yards. An excellent scheduling plan can provide a good raw material supply basis for steel enterprises. It is of great significance to improve the efficiency of steel production, reduce unnecessary operating waste and management costs, and realize scientific management of steel production. Objective: This patent aims to optimize the total material transportation time and equipment utilization balance within a single operation plan of the stacker-reclaimer involved in the raw material yard. Methods: A multi-objective optimization model for the stacker reclaimer is established, and the Reverse learning and Population Competitive-NSGA II (RPC-NSGA II) algorithm is introduced for solving. This algorithm uses reverse learning and population competition mechanism to improve the convergence and diversity of the algorithm. Results: The proposed method was experimentally verified in a raw material yard with a 360m2 sintering machine and a bulk material port. The method converges well and obtains a Pareto front with a uniform distribution. Compared with the actual scheduling plan, the scheduling plan under the optimal compromise solution reduces the maximum completion time by 11.23 minutes and increases the equipment utilization balance rate by 11.70%. Conclusion: The proposed method can consider the material transportation time and equipment utilization balance, which is of great significance for the optimized use of the stacker reclaimer in steel enterprises and the quality assurance of raw material supply. © 2023 Bentham Science Publishers.

关键词： multiobjective optimization

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An Improved Hunger Games Search Algorithm-based multi-peak MPPT control for PV System under Partial Shading

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Recent Patents on Mechanical engineering 2023年第4期16卷 261-273页

作者： Yi, Lingzhi Ma, Hao Wang, Yahui Liu, Jiangyong Shi, Hao Cheng, Siyue College of Automation and Electronic Engineering Xiangtan University Hunan Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

Background: In photovoltaic power generation systems, partial shading may cause the PV array to mismatch, thus leading to multi-peak output characteristics, which makes the conventional Maximum Power Point Tracking (MPPT) algorithm easily fall into local extremes and cause power loss. Objective: The study aimed to accurately and quickly track the maximum power point of PV arrays in partial shading through swarm intelligence algorithms. Methods: Based on the above, a MPPT control algorithm based on Chaos Adaptive Hunger Games Search with Dynamic Lévy Mutation Strategy (CAHGSL) is proposed in this paper. By introducing an improved logistics chaos map initialization population, a nonlinear adaptive convergence factor and a dynamic Lévy mutation strategy enhance their ability to jump out of local extremes during multi-peak MPPT and improve their tracking speed and efficiency. Results: Under the three working conditions, the tracking efficiency of the MPPT algorithm proposed in this paper has been achieved by more than 99.5% in an average time of 0.152s, which is higher tracking efficiency compared to the PO, PSO, and HGS algorithms. Conclusion: The results show that the MPPT algorithm proposed in this paper can balance the tracking speed and efficiency with less power oscillation during the tracking process, and can ensure stable output after convergence. The method proposed in this paper is helpful to improve the output power of PV arrays under partial shading. © 2023 Bentham Science Publishers.

关键词： Efficiency

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Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM

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Recent Patents on Mechanical engineering 2023年第1期16卷 72-86页

作者： Yi, Lingzhi Zhu, Jiang Wang, Yahui Liu, Jiangyong Wang, Shitong Liu, Bo College of Automation and Electronic Information Xiangtan University Hunan Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

Background: Accurate power load forecasting is significant in ensuring power load plan-ning, reliability and economical operation. The traditional power load is easily affected by climate, en-vironment, data type and other factors, resulting in the problem of poor forecasting accuracy. Therefore, it is necessary to study power load forecasting. Objective: Through machine learning, dimension reduction method and intelligent optimization algorithm, the accuracy of load forecasting is improved Methods: In order to fully extract load information and improve the accuracy of short-term load forecasting for campus electricity, an improved combination of orthogonal dimensionality reduction and Bilstm is proposed to optimize the hyperparameters in BiLSTM using an improved gray wolf algorithm. Firstly, using the advantages of principal component analysis (PCA) and Locality Preserving Projection (LPP) to maintain the global and local structure of the data, respectively, the Orthogonal PCA-LPP (OPCA-LPP) dimensionality reduction method is proposed to reduce the dimensionality of the original multidimensional data. Finally, the dimensionality-reduced data is used as the input of BiLSTM and op-timized by the improved Gray Wolf algorithm, which can enhance the model's prediction capability and thus achieve accurate prediction of short-term electric load. Results: The Mae and RMSE of this paper are 1.6585 and 1.7602, respectively. The results show that the method proposed in this paper is reasonable Conclusion: This method is applied to power load forecasting. The comparative experimental results show that this method reduces the dimension of data input, simplifies the complexity of network input data, and improves load forecasting accuracy. Compared with other methods, it can improve load forecasting accuracy and provide a basis for formulating reasonable power grid operation modes and bal-anced power grid dispatching. © 2023 Bentham Science Publishers.

关键词： Principal component analysis

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Task Offloading of Intelligent Building Based on Dependency-Aware in Edge Computing

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Recent Patents on Mechanical engineering 2023年第5期16卷 373-385页

作者： Lingzhi, Yi Jianxiong, Huang Yahui, Wang Jiao, Long Bote, Luo Jiangyong, Liu College of Information Engineering Xiangtan University& Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China Willfar Information Technology Co. Ltd Hunan Changsha410205 China

Background: With the rapid development of artificial intelligence, the traditional cloud computing model has serious bandwidth and energy consumption problems when storing and pro-cessing massive amounts of raw data. To address this problem, recent patents have investigated methods for intelligent task offloading and allocation in mobile edge computing. Objective: A Directed Acyclic Graph (DAG) task unloading model is established to reduce the problem of task delay and energy consumption in edge networks. At the same time, Modified Tuna Swarm Optimization (MTSO) is used to improve execution efficiency. Methods: Firstly, this paper integrates (i) inter-task dependencies;(ii) heterogeneity of computing re-sources in the edge network;and (iii) interference of wireless channels in the edge network. A DAG task offloading model is developed to reduce latency and energy consumption. The end users are guided to offload their tasks/sub-tasks to the most appropriate servers in the edge network, thus minimizing the end-to-end latency of all tasks in the edge network. Secondly, the MTSO algorithm is used to coordinate the dependencies and priorities of subtasks to improve execution efficiency. Results: The experimental results show that when the number of users including subtasks is 10, the final edge server utilization rate is as high as 92%. A more fine-grained segmentation scheme can reduce the average delay of tasks and improve the utilization rate of edge servers. Conclusion: The approach proposed in this paper reduces the end-to-end latency and improves resource utilization in complex applications under the premise of ensuring task dependency. It will relieve the pressure on the cloud and has certain engineering application value. © 2023 Bentham Science Publishers.

关键词： Mobile edge computing

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Soft Short-circuit Fault Diagnosis of the Lithium-ion Battery Pack Based on an Improved EKF Algorithm

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Recent Patents on Mechanical engineering 2023年第2期16卷 138-149页

作者： Yi, Lingzhi Cai, Xinkun Wang, Yahui Luo, Bote Liu, Jiangyong Liu, Bo College of Automation and Electronic Engineering Xiangtan University Hunan Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China Xpeng Motors-International User Development and Service Center Guangdong Guangzhou510700 China

Background: Lithium-ion batteries are widely used in new energy vehicles and energy storage systems due to their superior performance. However, lithium batteries are prone to safety problems in the use process, so the fault diagnosis technology of lithium batteries has attracted more attention. Objective: This study aimed to ensure the safety of lithium batteries and accurately and timely diagnose the soft short circuit (soft SC) fault of lithium battery Methods: Aiming at the energy storage lithium battery pack, this study proposed a soft short-circuit fault diagnosis method for the lithium-ion battery pack based on the improved Extended Kalman Filter (EKF) algorithm. First, the 1st-order RC equivalent circuit model of normal battery and soft SC fault battery was established, and model parameters were identified using Recursive Least Squares with Forgetting Factor (FFRLS). Then, using the improved EKF, the state of charge (SOC) of a single cell was estimated, and the difference between the calculated SOC and the estimated SOC by the coulomb count-ing method was used to detect soft SC faults and compared them with the reference data. Finally, the SC resistance value indicated the severity of the fault. Results: The proposed method could accurately diagnose the soft short circuit fault, and the error was found to be lower than the traditional EKF algorithm. The estimation error was about 0.4% for the battery with slight failure and about 1.5% for the battery with serious failure. Conclusion: The experimental results showed that the improved EKF algorithm could estimate the SOC difference more accurately, and the effect of soft SC fault diagnosis was better. At the same time, it could quantitatively identify the size of the short circuit resistance, which is very helpful for the subse-quent management of the battery system. © 2023 Bentham Science Publishers.

关键词： Lithium-ion batteries

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energy Saving Optimal Operation Strategy of Freight Trains under Complex Scenarios

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Recent Patents on Mechanical engineering 2023年第4期16卷 237-250页

作者： Yi, Lingzhi Xie, Cheng Wang, Yahui Liu, Jiangyong Yi, Chuyang Jiang, Wenbo College of Automation and Electronic Engineering Xiangtan University Hunan Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China Yongan Application Workshop of Fuzhou Locomotive Depot of Nanchang Railway Bureau Fujian Yongan366000 China

Background: For the optimization of energy-saving driving of freight trains in complex operating environments, the use of reasonable train maneuvering methods can largely reduce the energy consumption of train traction. Recent patents on energy-efficient maneuvering strategies for complex scenarios of freight trains have been researched. Objective: Using the receding horizon algorithm and the improved NSGA-II algorithm to solve the target speed curve of freight trains to cope with the complex and changing operating environment, and to explore the recent patents of energy-saving maneuvering strategies for freight trains and methods. Methods: The recent patents of energy-efficient maneuvering strategies for freight trains in complex scenarios are investigated in this research. A multi-objective optimization model for freight train maneuvering with electrical phasing was developed with the objectives of reducing the traction energy consumption and running time of the train. A method for determining the optimal operating conditions of freight trains under complex line conditions is proposed. The offline optimization of the target speed curve under the electrical phasing constraints of freight trains and the online adjustment under the temporary speed restriction (TSR) are achieved by using the RH-INSGA-II (receding horizon-improved NSGA-II) algorithm. Results: Combined with an actual freight railroad line data as an example, simulation experiments were conducted and verified with HXD1 electric locomotive hauling 50 C80 wagons. Conclusion: The speed curve considering the split-phase constraint can effectively reduce the traction energy consumption. The electrical split-phase constraint affects the whole speed optimization process, not only the speed curve at the split-phase zone. Although the traction energy consumption is increased with the addition of the TSR on the line, the RH-INSGA-II algorithm dynamically changes the sequence of optimal train maneuvering conditions

关键词： energy utilization

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Abnormal Status Detection of Catenary Based on TSNE Dimensionality Reduction Method and IGWO-LSSVM Model

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Recent Patents on Mechanical engineering 2023年第3期16卷 188-202页

作者： Lingzhi, Yi Guo, Yu Yahui, Wang Tengfei, Dong Huang, Yu Haixiang, She College of Automation and Electronic Engineering Xiangtan University Hunan Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China Pu'er Infrastructure section of China Railway Kunming Group Co. Ltd Yunnan Kunming665000 China

Background: Catenary is a crucial component of an electrified railroad's traction power supply sys-tem. There is a considerable incidence of abnormal status and failures due to prolonged outside exposure. Driving safety will be directly impacted if an abnormal status or failure occurs. Currently, catenary detection vehicles are the most often utilized technique for gathering data and identifying faults based on manual experience. However, this technology cannot meet the demands of prompt detection and correction of faults in railways engineering due to its extremely low work efficiency. Objective: Based on the above, an abnormal status detection method of catenary based on the improved gray wolf (IGWO) algorithm optimized the least squares support vector machine (LSSVM) with the t-distributed stochastic neighbor embedding (TSNE) is proposed in this paper. In order to improve the accuracy of catenary abnormal status detection and shorten the detection time. Methods: Firstly, the TSNE dimensionality reduction technology is used to reduce the original catenary data to three-dimensional space. Then, in order to address the issue that the parameters of the LSSVM detection model are hard to determine, the improved GWO algorithm is used to optimize the penalty factor and kernel parameter in the LSSVM and establish the TSNE-IGWO-LSSVM catenary abnormal status detection model. Finally, contrasting experimental results of different detection models. The T-distributed Stochastic Domain Embedding (TSNE) is an improved nonlinear dimensionality reduction method based on the Stochastic Neighbor Embedding (SNE). TSNE no longer adopts the distance invariance in linear dimensionality reduction methods such as ISOMAP. TSNE is much better than the linear dimensionality reduction method in the reduction degree of the original dimension. The GWO algorithm, which is frequently used in engineering re-search, has the advantages of a simple model, great generalization capability, and good op

关键词： Support vector machines

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Railroad Catenary Insulator Fault Detection Based on Improved Faster R-CNN

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Recent Patents on Mechanical engineering 2024年第4期17卷 243-259页

作者： Yi, Lingzhi Dong, Tengfei Wang, Yahui She, Haixiang Yi, Chuyang Yu, Guo College of Automation and Electronic Engineering Xiangtan University & Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Changsha410082 China Pu'er Infrastructure Section of China Railway Kunming Group Co. Ltd Yunnan Kunming665000 China Yongan Application Workshop of Fuzhou Locomotive Depot of Nanchang Railway Bureau Fujian Yongan366000 China

Background: The railroad catenary insulator, which is a crucial component of the cate-nary system and is situated between the pillar and wrist arm, is crucial for electrical conductor isolation, electrical equipment insulation, mechanical load bearing, anti-fouling, and anti-leakage. The catenary insulators will experience tarnished flash, breakage, insulation strength deterioration, and other issues as a result of the long-term outside unfavorable working circumstances. The train electrical system's ability to operate normally is greatly hampered by these problems. Although there are many patents and articles related to insulator fault detection, the precision is not high enough. Therefore, it is crucial to improve the precision of catenary insulator fault detection. Objective: An improved region-based convolutional neural networks (Faster R-CNN)-based fault detection method for railway catenary insulators is proposed in response to the long detection time of the conventional railroad catenary insulator fault, the low precision of the catenary insulator fault detection for occlusion and truncation, the poor performance of multi-scale object detection, and the processing of class unbalance problem. Methods: The Faster R-CNN is optimized from four perspectives: feature extraction, feature fusion, candidate box screening, and loss function, in accordance with the properties of the catenary insulator. First, to solve the problem of multi-scale catenary insulator fault detection, convolutional block attention module (CBAM) and feature pyramid network (FPN) are used to fuse the deep feature and shallow features of the image. This results in a feature map with more critical semantic information and higher resolution. After that, the weighted non-maximum suppression (WNMS) algorithm improved by distance-intersection over union (DIOU) and Gaussian weighting function is used instead of the traditional NMS algorithm, which effectively introduces the overlap of detection frame

关键词： Feature extraction

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Adaptive Robust Optimization Based on Data-driven Uncertainties of Source and Load for Microgrid Operation 1

Adaptive Robust Optimization Based on Data-driven Uncertaint...

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1st International Conference on Cyber-energy Systems and Intelligent energy, ICCSIE 2022

作者： Li, Zibin Tan, Mao Ren, Yuling Jiang, Hongwei Xiangtan University School of Automation and Electronic Information Xiangtan China Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China

ISBN: (纸本)9781665452151

The strong uncertainty of renewable energy output and load demand makes the stable operation of microgrids a challenging and important issue. However, the scheduling methods based on deterministic models cannot accurately describe the influence of uncertainties on operation of microgrids. To address this problem, this paper proposes a two-stage adaptive robust optimal scheduling model (TSARO) that considers both source and load uncertainties. The model first adopts the dirichlet process mixture model (DPMM) to perform cluster analysis and parameter estimation on massive historical data, and constructs a data-driven uncertainty set of source and load. Then, based on the uncertainty set, the TSARO model aiming at minimizing the microgrid operation cost is developed under the worst-case scenario. Finally, this paper solves the optimization model using column constraint generation algorithm (C&CG) to obtain the day-ahead power dispatching plan. Simulation results show that the proposed model in this paper has better economic benefits compared with several classical optimization models. © 2023 IEEE.

关键词： Cluster analysis

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