Short-term load forecasting of power system based on genetic algorithm improved BP neural network algorithm

Jian-jun, XU; Pan, Lichao; Sun, Yi; Yuan, Tianyi

doi:10.1109/aeeca55500.2022.9919006

Cited by 7 publications

(1 citation statement)

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“…The addition of momentum terms is used to change the learning rate of the B-P algorithm with momentum instead of a constant value in the training process [18]. The momentum items interfered with the adjustment of the connection weight.…”

Section: Improved Back-propagation Algorithmmentioning

confidence: 99%

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Yu,

Yang

2023

ENERGY

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Taking an industrial park as an example, this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources (DERs). The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data. To predict and analyze the load growth of the industrial park, an improved back-propagation algorithm is employed. Furthermore, the study classifies users within the industrial park according to their specific power consumption and supply requirements. This user segmentation allows for the introduction of three constraints: node voltage, wire current, and capacity of DERs. By incorporating these constraints, the study constructs an optimization model for the distribution network in the industrial park, with the objective of minimizing the total operation and maintenance cost. The primary goal of these optimizations is to address the needs of DERs connected to the distribution network, while simultaneously mitigating their potential adverse impact on the network. Additionally, the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

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Section: Improved Back-propagation Algorithmmentioning

confidence: 99%

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Yu,

Yang

2023

ENERGY

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Edge Computing Enabled Lightweight Neural Network for User Preference Based Customization Recommendation Mechanism

Li,

Sun,

et al. 2024

Internet Technology Letters

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The rapid development of computer and network technologies has led to an explosion of information, ushering in an era of e‐commerce dominated by online shopping. This shift underscores the need for precise, efficient, and customized recommendation systems. Traditional search engines struggle to cater to users' diverse information needs, leading to information overload and “dark information.” The customization recommendation systems emerge as a solution, leveraging user data to discern product correlations and formulate recommendations. However, remote server latency poses a challenge for real‐time recommendations. In this regard, we necessitate the exploration of edge computing and propose an edge computing enabled lightweight neural network for a user preference‐based customization recommendation mechanism including the edge computing based user preference model, the customization preference relevance, and the lightweight neural network‐based customization recommendation. In particular, the user preference model quantifies user affinity toward products and a preference connection pattern that ensures a clean relationship map by mitigating attribute interference, while the customization recommendation integrates back‐propagation for nonlinear input–output mappings, achieving generalization while maintaining efficiency. Experimental results indicate that the proposed mechanism can enhance customization and efficiency in recommendation systems when compared to the state‐of‐the‐art methods in terms of the recommendation accuracy, error rate as well as iterations.

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A Neural Network Model for Predicting User Load and Photovoltaic Output in Home Microgrids

Wu,

Luo,

2024

2024 9th Asia Conference on Power and Electrical Engineering (ACPEE)

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Short-term load forecasting of power system based on genetic algorithm improved BP neural network algorithm

Cited by 7 publications

References 0 publications

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Edge Computing Enabled Lightweight Neural Network for User Preference Based Customization Recommendation Mechanism

A Neural Network Model for Predicting User Load and Photovoltaic Output in Home Microgrids

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