Introduction to power market data and their characteristics
Modeling load forecasting uncertainty using deep learning models
Data-driven load data cleaning and its impacts on forecasting performance
Generalized cost-oriented load forecasting in economic dispatch
A monthly electricity consumption forecasting method
Data-driven pattern extraction for analyzing market bidding behaviors
Stochastic optimal offering based on probabilistic forecast on aggregated supply curves
Power market simulation framework based on learning from individual offering strategy
Deep inverse reinforcement learning for reward function identification in bidding models
The subspace characteristics and congestion identification of LMP data
Online transmission topology identification in LMP-based markets
Day-ahead componential electricity price forecasting
Quantifying the impact of price forecasting error on market bidding
Virtual bidding and FTR speculation based on probabilistic LMP forecasting
Abnormal detection of LMP scenario and data with deep neural networks.