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Zhengmei Lu
Abstract
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State-of-the-art techniques for modelling of uncertainties in active distribution network planning: a review
10.1016/j.apenergy.2019.01.211 · 2019
A survey on state estimation techniques and challenges in smart distribution systems
10.1109/tsg.2018.2870600 · 2019
Demand response optimization for the enhancement of the distribution system’s operation
2025
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
10.1016/j.apenergy.2022.119269 · 2022
Review of smart meter data analytics: applications, methodologies, and challenges
10.1109/tsg.2018.2818167 · 2019
A review of distribution network applications based on smart meter data analytics
10.1016/j.rser.2023.114151 · 2024
A review on distribution system state estimation uncertainty issues using deep learning approaches
10.1016/j.rser.2023.113752 · 2023
Applications of physics-informed neural networks in power systems: a review
10.1109/tpwrs.2022.3162473 · 2023
Network partition-based zonal voltage control for distribution networks with distributed PV systems
10.1109/tsg.2017.2648779 · 2018
Confidence 0%
Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling
10.1109/tpwrs.2012.2187804 · 2012
Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling
10.1186/s41601-023-00306-w · 2023
Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm
10.1016/j.measurement.2024.114223 · 2024
MissForest—non-parametric missing value imputation for mixed-type data
10.1093/bioinformatics/btr597 · 2012
BRITS: bidirectional recurrent imputation for time series
2018
GAIN: Missing Data Imputation using Generative Adversarial Nets
2018
SAITS: Self-attention-based imputation for time series
10.1016/j.eswa.2023.119619 · 2023
Filling the gaps: multivariate time series imputation by graph neural networks
2022
Recurrent neural networks for multivariate time series with missing values
10.1038/s41598-018-24271-9 · 2018
Matrix completion for low-observability voltage estimation
10.1109/tsg.2019.2956906 · 2020
A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection
10.1109/tpami.2024.3443141 · 2024
A multi-scale spatial-temporal graph neural network-based method of multienergy load forecasting in integrated energy system
10.1109/tsg.2023.3315750 · 2024
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
2016
Unrolled spatiotemporal graph convolutional network for distribution system state estimation and forecasting
10.1109/tste.2022.3211706 · 2023
Do Transformers Really Perform Badly for Graph Representation?
2021
Train short, test long: attention with linear biases enables input length extrapolation
2022
Physics-informed graphical neural network for power system state estimation
10.1016/j.apenergy.2023.122602 · 2024
DC3: A learning method for optimization with hard constraints
2021
Network reconfiguration in distribution systems for loss reduction and load balancing
10.1109/61.25627 · 1989
A linear branch flow model for radial distribution networks and its application to reactive power optimization and network reconfiguration
10.1109/tsg.2020.3039984 · 2021
Physics-aware neural networks for distribution system state estimation
10.1109/tpwrs.2020.2988352 · 2020
Power flow model for medium-voltage distribution systems considering measurement and structure characteristics
10.35833/mpce.2023.000035 · 2024
Attention is All you Need
2017
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
2022
A time series is worth 64 words: Long-term forecasting with Transformers
2023
Xception: Deep learning with depthwise separable convolutions
2017
Optimal sizing of capacitors placed on a radial distribution system
10.1109/61.19266 · 1989
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
2008
Optimal sizing of capacitors placed on a radial distribution system
10.1109/61.19266 · doi-reference
Power flow model for medium-voltage distribution systems considering measurement and structure characteristics
10.35833/mpce.2023.000035 · doi-reference
Physics-aware neural networks for distribution system state estimation
10.1109/tpwrs.2020.2988352 · doi-reference
A linear branch flow model for radial distribution networks and its application to reactive power optimization and network reconfiguration
10.1109/tsg.2020.3039984 · doi-reference
Network reconfiguration in distribution systems for loss reduction and load balancing
10.1109/61.25627 · doi-reference
Physics-informed graphical neural network for power system state estimation
10.1016/j.apenergy.2023.122602 · doi-reference
Unrolled spatiotemporal graph convolutional network for distribution system state estimation and forecasting
10.1109/tste.2022.3211706 · doi-reference
A multi-scale spatial-temporal graph neural network-based method of multienergy load forecasting in integrated energy system
10.1109/tsg.2023.3315750 · doi-reference
A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection
10.1109/tpami.2024.3443141 · doi-reference
Matrix completion for low-observability voltage estimation
10.1109/tsg.2019.2956906 · doi-reference
Recurrent neural networks for multivariate time series with missing values
10.1038/s41598-018-24271-9 · doi-reference
SAITS: Self-attention-based imputation for time series
10.1016/j.eswa.2023.119619 · doi-reference
MissForest—non-parametric missing value imputation for mixed-type data
10.1093/bioinformatics/btr597 · doi-reference
Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm
10.1016/j.measurement.2024.114223 · doi-reference
Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling
10.1186/s41601-023-00306-w · doi-reference
Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling
10.1109/tpwrs.2012.2187804 · doi-reference
Network partition-based zonal voltage control for distribution networks with distributed PV systems
10.1109/tsg.2017.2648779 · doi-reference
Applications of physics-informed neural networks in power systems: a review
10.1109/tpwrs.2022.3162473 · doi-reference
A review on distribution system state estimation uncertainty issues using deep learning approaches
10.1016/j.rser.2023.113752 · doi-reference
A review of distribution network applications based on smart meter data analytics
10.1016/j.rser.2023.114151 · doi-reference
Review of smart meter data analytics: applications, methodologies, and challenges
10.1109/tsg.2018.2818167 · doi-reference
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
10.1016/j.apenergy.2022.119269 · doi-reference
A survey on state estimation techniques and challenges in smart distribution systems
10.1109/tsg.2018.2870600 · doi-reference
State-of-the-art techniques for modelling of uncertainties in active distribution network planning: a review
10.1016/j.apenergy.2019.01.211 · doi-reference