Data Analytics in Power Markets
portes grátis
Data Analytics in Power Markets
Zheng, Kedi; Wang, Yi; Guo, Hongye; Chen, Qixin
Springer Verlag, Singapore
10/2022
273
Mole
Inglês
9789811649776
15 a 20 dias
Descrição não disponível.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Power markets;bidding strategy;machine learning;price forecasting;load forecasting
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
