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Found 16 papers, 1 papers with code
A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency.
A White-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov-Arnold Network
However, due to the "black-box" nature, deep learning methods have long been blamed for their poor interpretability when modeling a physical system.
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance.
A Neural-Network-Embedded Equivalent Circuit Model for Lithium-ion Battery State Estimation
Equivalent Circuit Model(ECM)has been widelyused in battery modeling and state estimation because of itssimplicity, stability and interpretability. However, ECM maygenerate large estimation errors in extreme working conditionssuch as freezing environmenttemperature andcomplexcharging/discharging behaviors, in whichscenariostheelectrochemical characteristics of the battery become extremelycomplex and nonlinear. In this paper, we propose a hybridbattery model by embeddingneural networks as 'virtualelectronic components' into the classical ECM to enhance themodel nonlinear-fitting ability and adaptability.
Unsupervised and Interpretable Synthesizing for Electrical Time Series Based on Information Maximizing Generative Adversarial Nets
Conditional Generative Adversarial Nets, cGAN) require labeled dataset to train the model, which is demanding in practice because many field measurement data lacks descriptive labels.
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achieve optimal database performance.
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration
This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options.
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation
We propose a Generator structure consisting of a coarse network and a fine-tuning network.
An Iterative Bidirectional Gradient Boosting Approach for CVR Baseline Estimation
In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem.
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations
This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously.
Feeder Microgrid Management on an Active Distribution System during a Severe Outage
The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system.
SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid
Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages.
A TCN-based Hybrid Forecasting Framework for Hours-ahead Utility-scale PV Forecasting
The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model.
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles
When training the ProfileSR-GAN generator network, to make the generated profiles more realistic, we introduce two new shape-related losses in addition to conventionally used content loss: adversarial loss and feature-matching loss.
Generative Adversarial Network
Non-Intrusive Load Monitoring
+3
A Meta-learning based Distribution System Load Forecasting Model Selection Framework
This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework.
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy
Q learning is widely used to simulate the behaviors of generation companies (GenCos) in an electricity market.
