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A hybrid day-ahead electricity price forecasting framework based on time series

ENERGY 2023年 264卷

作者： Xiong, Xiaoping Qing, Guohua Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Zhuang Peoples R China

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF.

关键词： Electricity price forecasting Copula entropy Feature selection Variational mode decomposition optimization algorithm Long short-term memory

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Layout optimization Design for Manufacturing Workshop

Layout Optimization Design for Manufacturing Workshop

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第34届中国控制与决策会议

作者： Dan Liu Yan Liu Liwei Li Mouquan Shen College of Electrical Engineering and Control Science Nanjing Technology University Zhejiang Zhongde Automatic Control Science and Technology Joint Stock Co. Ltd. School of Automation Nanjing University of Information Science & Technology

This paper considers a layout optimization problem in manufacturing *** the characteristics of each work areas and logistics into consideration,a mathematical model with an objective and multiple constraints is *** model can ensure that there is no overlap between work areas,as well as between work areas and logistics *** proposed mathematical model can be solved by heuristic ***,a numerical simulation is used to verify the effectiveness of our model.

关键词： Factory layout heuristics optimization algorithm

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A New Approach for Educational Data Analytics with Wearable Devices 21

A New Approach for Educational Data Analytics with Wearable ...

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21st IEEE International Conference on Advanced Learning Technologies (ICALT)

作者： Zhou, Zhenxing Tam, Vincent W. L. Lui, King-Shan Lam, Edmund Y. Kong, Runzhi Hu, Xiao Law, Nancy Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Hong Kong Fac Educ Hong Kong Peoples R China

ISBN: (纸本)9781665441063

The rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.

关键词： educational data analytics wearable device optimization algorithm

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Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 60卷

作者： Li, Xudong Yang, Weijia Zhao, Zhigao Wang, Ran Yin, Xiuxing Liu, Pan Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints;(2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%;(2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

关键词： Hybrid energy system Hydropower Risk optimization algorithm Decarbonization

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Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly algorithm and a Comparison Study with GA, PSO, ABC, and GWO

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ENERGIES 2023年第18期16卷 6611-6611页

作者： Amiri, Farhad Eskandari, Mohsen Moradi, Mohammad Hassan Bu Ali Sina Univ Fac Engn Elect Engn Dept Hamadan *** Iran Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.

关键词： autonomous microgrids battery energy storage systems (BESSs) cascaded controller inverter-interfaced energy resources optimization algorithm renewable energy resources virtual inertia

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An optimized device-to-device (D2D) blockchain network for the insurance industry

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第6期36卷 e5446-e5446页

作者： Bilgehan, Bulent Sabuncu, Ozlem Near East Univ Dept Elect & Elect Engn Mersin 10 Lefkosa Trnc Turkiye

Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two-stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step-by-step calculation method that is not feasible under real-time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.

关键词： blockchain D2D fifth-generation mobile edge computing optimization algorithm the insurance industry

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Integrating Fuzzy Systems with optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2013年第13期43卷 23-29页

作者： Ren, Huilin Gou, Jin Huaqiao Univ Sch Informat Comp Sci & Technol Xiamen 361021 Fujian Peoples R China

In this paper, a fuzzy system fusion structure that can integrate multiple fuzzy systems is proposed. The novel approach is made up of three phases: fuzzy knowledge encoding, the initial fuzzy rule bases acquisition and fuzzy system integration. In the fuzzy knowledge encoding stage, the data set is coded as positive integer according to encoding strategy. The initial fuzzy rule bases are derived from expert groups, evolutionary algorithms or other methods. In the fuzzy system integration phrase, an optimal fuzzy system or an almost optimal fuzzy system is derived from the initial population by using the multi-mutation particle swarm optimization algorithm. Experimental evaluation on different kinds of methods shows that our proposed algorithm can improve the performance of the fusion fuzzy system.

关键词： fuzzy system optimization algorithm pso

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Study of a New Global optimization algorithm Based on the Standard PSO

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JOURNAL OF optimization THEORY AND APPLICATIONS 2013年第3期158卷 935-944页

作者： Yang, B. Cheng, L. Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

In this paper, a new global optimization algorithm is developed, which is named Particle Swarm optimization combined with Particle Generator (PSO-PG). Based on a series of comparable numerical experiments, we show that the calculation accuracy of the new algorithm is greatly improved and optimization efficiency is increased as well, in comparison with those of the standard PSO. It is also found that the optimization results obtained from PSO-PG are almost independent of the coefficients adopted in the algorithm.

关键词： Particle swarm optimization (PSO) Particle generator optimization algorithm Global optimization

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Equitable Top-k Results for Long Tail Data

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Proceedings of the ACM on Management of Data 2023年第4期1卷 1-24页

作者： Md Mouinul Islam Mahsa Asadi Senjuti Basu Roy New Jersey Institute of Technology Newark NJ USA

For datasets exhibiting long tail phenomenon, we identify a fairness concern in existing top-k algorithms, that return a "fixed" set of k results for a given query. This causes a handful of popular records (products, items, etc) getting overexposed and always be returned to the user query, whereas, there exists a long tail of niche records that may be equally desirable (have similar utility). To alleviate this, we propose θ-Equiv-top-k-MMSP inside existing top-k algorithms - instead of returning a fixed top-k set, it generates all (or many) top-k sets that are equivalent in utility and creates a probability distribution over those sets. The end user will be returned one of these sets during the query time proportional to its associated probability, such that, after many draws from many end users, each record will have as equal exposure as possible (governed by uniform selection probability). θ-Equiv-top-k-MMSP is formalized with two sub-problems. (a) θ-Equiv-top-k-Sets to produce a set S of sets, each set has k records, where the sets are equivalent in utility with the top-k set; (b) MaxMinFair to produce a probability distribution over S, that is, PDF(S), such that the records in S have uniform selection probability. We formally study the hardness of θ-Equiv-top-k-MMSP. We present multiple algorithmic results - (a) An exact solution for θ-Equiv-top-k-Sets, and MaxMinFair. (b) We design highly scalable algorithms that solve θ-Equiv-top-k-Sets through a random walk and is backed by probability theory, as well as a greedy solution designed for MaxMinFair. (c) We finally present an adaptive random walk based algorithm that solves θ-Equiv-top-k-Sets and MaxMinFair at the same time. We empirically study how θ-Equiv-top-k-MMSP can alleviate a equitable exposure concerns that group fairness suffers from. We run extensive experiments using 6 datasets and design intuitive baseline algorithms that corroborate our theoretical analysis.

关键词： equal exposure fairness long tail data optimization algorithm top-k retrieval

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Adaptive mean center of mass particle swarm optimizer for auto- localization in 3D wireless sensor networks

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第9期35卷

作者： Alhasan, Waseem Ahmad, Rami Wazirali, Raniyah Aleisa, Noura Shdeed, Weaam Abo Montfort Univ Dubai Sch Comp Sci & Informat Dubai U Arab Emirates Amer Univ Emirates Coll Comp Informat Technol Dubai 503000 U Arab Emirates Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia Syrian Virtual Univ Fac Informat Technol Damascus Syria

Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Wireless Sensor Networks WSNs-6LoWPAN Localization Range based optimization algorithm Particle Swarm Optimizer Irregular surfaces

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