Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction

Bayat, Nasrin; Park, Joon‐Hyuk

doi:10.1109/naps56150.2022.10012263

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Additionally, [31] employs an efficient metaheuristic technique based on artificial bee swarm optimization for optimal configuration. Bee swarm algorithms are effective for addressing large-scale nonlinear optimization problems, similar to particle swarm algorithms, which also do well in this domain [94]. These optimization algorithms are inspired by the intelligent behaviors of honey bees in collecting and processing nectar.…”

Section: Gighs With Local Demandmentioning

confidence: 99%

Exploring Green Hydrogen Applications and Optimization Methods in the Power Sector: A Comprehensive Review

Goodarzi,

2024

Preprint

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Section: Gighs With Local Demandmentioning

confidence: 99%

Exploring Green Hydrogen Applications and Optimization Methods in the Power Sector: A Comprehensive Review

Goodarzi,

2024

Preprint

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“…The main attributes of each relevant research work are presented in Table 3. PSO is an effective way of solving largescale non-linear optimization problems [129]. The reason for practicing PSO in DR optimization is because of the tendency to give impactful and accurate results [130].…”

Section: Pso-based Applications In Demand Responsementioning

confidence: 99%

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

et al. 2023

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The increase in energy consumption, environmental pollution issues, and low-carbon agenda has grown the research area of demand side management (DSM). DSM programs provide feasible solutions and significantly enhance the efficiency and sustainability of electrical distribution systems. This paper classifies and discusses the broad definition of DSM based on the comprehensive literature study considering demand response and energy efficiency. The implementation of Artificial Intelligence algorithms in DSM applications has been employed in many studies to help researchers make optimal decisions and achieve predictions by analyzing the massive amount of historical data. Owing to its simplicity and consistent performance in fast convergence time, Particle Swarm Optimization (PSO) is widely used as a part of the swarm AI algorithm and has become a prominent technique in the optimization process to exploit the full benefit of the demand-side program. The variants of PSO have been developed to overcome the limitations of the original PSO and solve the high complexity and uncertainty in the DSM optimization process. The proposed PSO-based algorithm can optimize consumers' consumption curves, reducing the peak demand and hence minimizing the electricity cost when integrated with the DR programs or EE measures. The research works of the PSO algorithm in DSM have seen an increasing trend in the past decade. Therefore, this paper reviewed the application of the PSO-based algorithm in DSM fields with some constraints and discussed the challenges from the previous work. The potential for new opportunities is identified so that PSO methods can be developed for future research.INDEX TERMS Demand side management (DSM), demand response (DR), energy efficiency (EE), metaheuristic algorithms, particle swarm optimization (PSO), swarm intelligence.

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“…Recent advances in deep neural networks (DNN) [1]- [4] and access to very large datasets with million annotated data especially for computer vision applications have led to state-of-the-art results in many problem domains such as object detection and scene classification [5]- [7]. For example, Faster-RCNN network [8] achieved the impressive results in recognition and localization of objects in natural scenes or GoogleNet [9] reached approximately to the human performance in classification of the ImageNet database [10].…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Ghasemi¹,

Mottaghian²,

Bayat³

2023

Preprint

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Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster-RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies.

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Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction

Cited by 7 publications

References 21 publications

Exploring Green Hydrogen Applications and Optimization Methods in the Power Sector: A Comprehensive Review

Exploring Green Hydrogen Applications and Optimization Methods in the Power Sector: A Comprehensive Review

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

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