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Study on deterministic and interval forecasting of electricity load based on multi-objective whale optimization algorithm and transformer model

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Du, Pei Ye, Yuxin Wu, Han Wang, Jianzhou Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

Scientific and accurate electricity load forecasting is crucial for realizing effective power dispatch and ensuring the security, reliability and economy of power system operation. To this end, this research proposes a hybrid framework using data processing and analysis methods, deep learning and a multi-objective optimization algorithm. The framework includes four modules: data processing and mining module, optimization module, forecasting module, and evaluation module. Specifically, in the data processing and mining module, a longitudinal data selection method is used to extract sequence similarity features, while distribution functions are applied to capture the statistical properties of the data. In the optimization module, the multi-objective whale optimization algorithm is adopted to fine-tune the hyperparameters of the Transformer model to construct an optimized Transformer model. In the forecasting module, deterministic and uncertainty predictions of the developed model and comparison methods are carried out using two electricity load datasets to get the final predicted values. Furthermore, in the evaluation module, several deterministic prediction evaluation metrics and three uncertainty evaluation metrics are introduced to evaluate the prediction abilities of the methods. Ultimately, the numerical results display that compared with the optimal benchmark model, the developed model using two datasets can enhance the improvement percentage of mean absolute percentage error by 35.9327% and 23.7584%, respectively, which demonstrates its higher prediction performance than benchmark models, improves the prediction accuracy of electricity load, and provides valuable insights and references for other energy prediction fields.

关键词： Electricity load forecasting Deterministic and interval forecasting Transformer multi-objective whale optimization algorithm

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multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast multi-Output Relevance Vector Regression Optimized by the multi-objective whale optimization algorithm

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ENERGIES 2019年第1期12卷 147页

作者： Xiong, Shenghua Wang, Chunfeng Fang, Zhenming Ma, Dan Tianjin Univ Coll Management & Econ 92 Weijin Rd Tianjin 300072 Peoples R China Tianjin Univ Financial Engn Res Ctr 92 Weijin Rd Tianjin 300072 Peoples R China

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China's three major regional emission exchanges.

关键词： carbon price forecasting variational mode decomposition fast multi-output relevance vector regression multi-objective whale optimization algorithm Chinese carbon emission exchange

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A novel hybrid system based on a new proposed algorithm-multi-objective whale optimization algorithm for wind speed forecasting

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APPLIED ENERGY 2017年 208卷 344-360页

作者： Wang, Jianzhou Du, Pei Niu, Tong Yang, Wendong Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

In recent years, managers and researchers have paid increasing attention to accurate and stable wind speed prediction due to its significant effect on power dispatching and power grid security. However, most previous research has focused only on enhancing either accuracy or stability, with few studies addressing the two issues, simultaneously. This task is challenging due to the intermittency and complex fluctuations of wind speed. Therefore, we proposed a novel hybrid system based on a newly proposed called the MOWOA, which includes four modules: a data preprocessing module, optimization module, forecasting module, and evaluation module. An effective decomposing technique is also applied to eliminate redundant noise and extract the primary characteristics of wind speed data. In order to obtain high accuracy, and stability for wind speed prediction simultaneously, and overcome the weaknesses of single objective optimization algorithms, the optimization module of the proposed MOWOA is utilized to optimize the weights and thresholds of the Elman neutral network used in the forecasting module. Finally, the evaluation module, which includes hypothesis testing, evaluation criteria, and three experiments, is introduced perform comprehensive evaluation on the system. The results indicate that the proposed MOWOA performs better than the two recently developed MOALO and MODA algorithms, and that the proposed hybrid model outperforms all sixteen models used for comparison, which demonstrates its superior ability to generate forecasts in terms of forecasting accuracy and stability.

关键词： Wind speed forecasting multi-objective whale optimization algorithm Hybrid forecasting system Forecasting accuracy and stability

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multi-objective whale optimization for Network Interface Selection in HWNs 30

Multi-Objective Whale Optimization for Network Interface Sel...

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30th IEEE International Conference on Enabling Technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Mefgouda, Brahim Idoudi, Hanen Univ Manouba Natl Sch Comp Sci Manouba Tunisia

ISBN: (纸本)9781665427890

The network interface selection (NIS) is an important function that should be executed to connect the user's equipment to the best available network anytime and anywhere by meeting the user/application QoS requirements. multi-attribute decision-making approaches (MADM) are commonly applied to model and solve NIS problems. Although they can rank networks quickly with a high precision, they suffer from two drawbacks. Firstl, the rank reversal problem (RRP). Second, The selection of the highest-ranking score network without considering user and/or service requirements. In this paper, we introduce a novel approach to solve the above-cited drawbacks. We model the weighting techniques as a multi-objective problem. Then, we apply an extended version of the whale optimization algorithm to obtain the decision criteria weights according to the traffic classes and the chosen MADM approaches. The obtained simulations clearly show that applying our approach with MADM approaches reduces the RRP and satisfies the user/service requirements.

关键词： Heterogeneous wireless networks Network interface selection multi-objective whale optimization algorithm

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The approach to multi-objective optimization for process parameters of dry hobbing under carbon quota policy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第9-10期121卷 6073-6094页

作者： Liu, Yifan Yan, Chunping Ni, Hengxin Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China

For process parameter optimization in high-speed dry hobbing, an optimization decision method is proposed in this study based on the multi-objective whale optimization algorithm (MOWOA). Firstly, according to the characteristics of high-speed dry hobbing, the processing time and processing error models are constructed;considering the carbon quota energy conservation and environmental protection policy, the processing cost model is established. Founded on the above models, the fitness function of the multi-objective optimization model is proposed. Afterward, on the basis of the traditional single-objective whale algorithm, the non-dominant set and crowding calculation method is introduced to establish a multi-objective optimization whale algorithm model. On this basis, the Pareto solution set is obtained. Finally, the actual decision case is compared to verify the effectiveness of the proposed method. Furthermore, the optimization data of MOWOA and several commonly multi-objective algorithms are compared to analyze the characteristics of the optimization solution set, thus verifying the superiority of the process parameter optimization method based on MOWOA.

关键词： Carbon quota High-speed dry hobbing multi-objective whale optimization algorithm Processing time Processing cost Processing error

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An Adaptive Classifier Based Approach for Crowd Anomaly Detection

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Computers, Materials & Continua 2022年第7期72卷 349-364页

作者： Sofia Nishath P.S.Nithya Darisini School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep *** this approach,Adaptive GoogleNet Neural Network Classifier with multi-objective whale optimization algorithm are applied to predict the abnormal video frames in the crowded *** use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking *** technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video *** use the multi-objective whale optimization algorithm to optimize the entire process and get the best *** performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation *** simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.

关键词： Abnormal event detection adaptive GoogleNet neural network classifier multiple instance learning multi-objective whale optimization algorithm

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