Study on load monitoring and demand side management strategy based on Elman neural network optimized by sparrow search algorithm

Fan, Yuanyuan; Sui, Tingyu; Peng, Kang; Sang, Yingjun; Huang, Fei

doi:10.1108/cw-07-2021-0199

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The training data and the iteration period are also guaranteed to be constant. The Particle Swarm Optimization algorithm (PSO) [40], Beetle Antennae Search algorithm (BAS) [41], Sparrow Search algorithm (SSA) [42], and MVO-optimized SVM are used with the same image feature parameters and time delay compensation, respectively, and the prediction effect is finally observed using the same set of test data. Since optimization algorithms are generally strongly stochastic, ten sets of tests are performed for each algorithm, and the Wilcoxon sign rank test [43] is performed between the RMSEs of the prediction results of the three comparison algorithms and the RMSEs of the MVO, respectively, to prove the optimization effectiveness of the MVO algorithm.…”

Section: Model Test Resultsmentioning

confidence: 99%

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Zhao,

Zhou,

et al. 2023

Sustainability

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In order to accurately predict the key operating parameters of waste incinerators, this paper proposes a prediction method based on big data and a Multi-Verse Optimizer deep learning model, thus providing a powerful reference for controlling the optimization of the incinerator combustion process. The key operating parameters that were predicted, according to the control objectives, were determined to be the steam flow, gas oxygen, and flue temperature. Firstly, a large amount of measurement data were collected, and 27 relevant control system parameters with a high correlation with the predicted variables were obtained via a mechanism analysis. The input variables of the prediction model were further determined using the improved WesselN symbolic transfer entropy algorithm. The delay time between the variables was found using a gray correlation coefficient, the prediction time was determined to be 6 min according to the delay time distribution of the flame feature, and the time delay compensation was applied to each parameter. Finally, the support vector machine was optimized using a Multi-Verse Optimization algorithm to complete the prediction of the key operating parameters. Experiments showed that the root mean square error of the proposed model for the three output variables—the steam flow, gas oxygen, and flue temperature—were 0.3035, 0.2477, and 1.6773, respectively, which provides a high accuracy compared to other models.

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Section: Model Test Resultsmentioning

confidence: 99%

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Zhao,

Zhou,

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points is of great significance, and monitoring points are selected according to the fluctuation coefficients of energy efficiency [2]. In order to overcome the complexity of sensor equipment in data acquisition, transmission, storage and analysis, and to achieve the purpose of condition monitoring, estimation and control, a load monitoring system for chemical enterprises has been proposed to collect energy consumption data and analyze energy consumption, and an Elman neural network based on the sparrow search algorithm was proposed to predict the change and distribution trends of electricity consumption in the future production cycles of enterprises [3]. The above literature summarizes the latest applications in the field of sensing and state acquisition in modern industry.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System

Xiao,

Jin,

Huang

et al. 2023

Electronics

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In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user’s electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately 2% and reduces the system delay by approximately 16%, so that the user’s electricity consumption information can be analyzed and processed faster.

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Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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Study on load monitoring and demand side management strategy based on Elman neural network optimized by sparrow search algorithm

Cited by 6 publications

References 9 publications

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System

Recent Versions and Applications of Sparrow Search Algorithm

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