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Recurrent neural network with density-based clustering for group pattern detection in energy systems

https://doi.org/10.1016/j.seta.2022.102308
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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inferredcites →https://doi.org/10.1016/j.enbuild.2021.111769openalex-snapshot
Delta-T-based operational signatures for operation pattern and fault diagnosis of building energy systems
inferredcites →https://doi.org/10.1016/j.enbuild.2022.111888openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.energy.2020.119397openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.eswa.2021.115518openalex-snapshot
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Review on deep learning applications in frequency analysis and control of modern power system
inferredcites →https://doi.org/10.1016/j.inffus.2020.08.003openalex-snapshot
Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection
inferredcites →https://doi.org/10.1016/j.ins.2017.08.043openalex-snapshot
Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem
inferredcites →https://doi.org/10.1016/j.ins.2021.08.039openalex-snapshot
Multi-level and relevance-based parallel clustering of massive data streams in smart manufacturing
inferredcites →https://doi.org/10.1016/j.knosys.2020.106548openalex-snapshot
ASRNN: A recurrent neural network with an attention model for sequence labeling
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A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions
inferredcites →https://doi.org/10.1109/comst.2019.2904897openalex-snapshot
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inferredcites →https://doi.org/10.1109/comst.2019.2916177openalex-snapshot
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Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Prediction and Correlation Analysis of Heating Energy Consumption in Four Residential Apartments with Sensitivity Analysis Using Long Short-Term Memory (Lstm) and Generalized Regression Neural Network (Grnn)
inferred← citeshttps://doi.org/10.1007/s12667-024-00704-5openalex-snapshot
Short-term customer-centric electric load forecasting for low carbon microgrids using a hybrid model
inferred← citeshttps://doi.org/10.1007/s13369-025-10559-2openalex-snapshot
Forecasting and Optimal Scheduling of Electric Vehicle Charging Demand: A Cluster and Decomposition-Based Optimized Hybrid Approach
inferred← citeshttps://doi.org/10.1016/j.comcom.2023.03.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ins.2023.119059openalex-snapshot
Fair oversampling technique using heterogeneous clusters
inferred← citeshttps://doi.org/10.1016/j.seta.2024.103976openalex-snapshot
Predictive and correlational analysis of heating energy consumption in four residential apartments with sensitivity analysis using long Short-Term memory and Generalized regression neural network models
inferred← citeshttps://doi.org/10.1038/s41598-023-32790-3openalex-snapshot
Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance
inferred← citeshttps://doi.org/10.1109/dsaa60987.2023.10302528openalex-snapshot
Empowering Urban Connectivity in Smart Cities using Federated Intrusion Detection
inferred← citeshttps://doi.org/10.1109/icotl59758.2023.10435340openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ijcnn60899.2024.10650496openalex-snapshot
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inferred← citeshttps://doi.org/10.32604/cmc.2023.039923openalex-snapshot
Fully Automated Density-Based Clustering Method
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Real-Time Structure Generation Based on Data-Driven Using Machine Learning
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