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Found 11 papers, 0 papers with code
LLMTune: Accelerate Database Knob Tuning with Large Language Models
Database knob tuning is a critical challenge in the database community, aiming to optimize knob values to enhance database performance for specific workloads.
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.
